Generalizing Monocular 3D Object Detection

Abhinav Kumar

REVIEW 3 major objections 4 minor 300 references

Generalizing Monocular 3D Object Detection

T0 review · 3 major / 4 minor · reviewed 2026-08-15 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read Monocular 3D detection generalizes to occlusions, new datasets, large objects, and camera heights through four targeted geometric fixes.

desk verdict Solid empirical thesis on generalizing Mono3D; the SeaBird proof has a domain error that must be fixed, but the experiments stand. read the letter →

arxiv 2508.19593 v1 pith:NGXEHWTU submitted 2025-08-27 cs.CV

Abhinav Kumar This is my paper · ORCID

classification cs.CV

keywords monocular3Dobjectdetectiondifferentiablenon-maximumsuppressiondepthequivariancescale-equivariantsteerableconvolutionbird's-eye-viewsegmentationdicelosscameraheightgeneralizationprojectivegeometry

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Monocular 3D object detection estimates an object's position, dimensions, and orientation from a single image, but trained detectors degrade when occlusion, dataset, object size, or camera height leave the training distribution. The thesis claims each of these four failures has a specific, fixable cause and supplies a mechanism for each. For occlusions it makes non-maximum suppression differentiable and trains on post-suppression boxes; for datasets it builds a backbone equivariant to depth translations in the projective manifold. For large objects it proves dice loss resists depth noise better than regression losses and applies it in bird's-eye-view segmentation; for unseen camera heights it averages regressed depth with ground-plane depth, whose errors trend in opposite directions. If the claims hold, monocular detectors become more robust to hard cases, new domains, large vehicles, and different ego heights without extra sensors.

What carries the argument

The load-bearing objects are four mechanisms. For GrooMeD-NMS, non-maximum suppression is rewritten as a matrix resco ring $r \approx (I + P)^{-1} s$, then simplified through unsupervised grouping and masking to $r_G = (I_G - M_G \odot P_G) s_G$, making NMS a single differentiable layer. For DEVIANT, Corollary 1.1 reduces a depth translation $t_Z$ with no rotation and a locally planar patch to a scale transformation $s = 1 + \frac{t_Z o}{p}$, which justifies replacing vanilla convolutions with scale-equivariant steerable blocks. For SeaBird, the key identity is the dice-loss gradient variance $\mathrm{Var}(\epsilon) = \frac{1}{\ell^2}\operatorname{Erf}\left(\frac{\ell}{\sqrt{2}\sigma}\right)$, which decreases with object length $\ell$ and makes dice loss more noise-robust than L1 or L2 depth regression for large objects. For CHARM3R, the mechanism is the opposite extrapolation slope of regressed depth (negative) versus ground-plane depth (positive) under a height change $\Delta H$, so averaging the two inside the network cancels the trends.

What would settle it

Render a curved, non-planar object with the camera translated purely forward and measure DEVIANT's scale-equivariance error against a vanilla CNN; if it does not drop, the local-planarity assumption behind the claimed depth equivariance fails.

Watch

Extended reading notes

Core claim

The thesis claims that four distinct failure modes of monocular 3D detection share a common root: a mismatch between how detectors are trained or structured and the geometry they must interpret. GrooMeD-NMS gives a closed-form, differentiable matrix formulation of non-maximum suppression, so the network is trained on the final kept boxes rather than the pre-suppression proposals, improving hard and occluded objects on KITTI. DEVIANT, built from scale-equivariant steerable convolutions, is equivariant to depth translations in the projective manifold under local planar-object assumptions, giving consistent depth estimates and better cross-dataset transfer. SeaBird demonstrates that large-object failures are not only data imbalance or receptive-field problems but also noise sensitivity of depth regression, and it proves that dice loss has lower gradient variance for large objects, leading to improved detection when applied as bird's-eye-view foreground segmentation. CHARM3R proves that regressed depth and ground-plane depth have opposite mean-error trends under camera-height changes and that averaging them inside the model cancels those trends, improving detection at unseen ego heights on CARLA.

Load-bearing premise

The load-bearing premise is that, for a pure forward camera translation, each visible object patch is nearly parallel to the image plane so the projection change is simply a scale; when surfaces are strongly slanted or curved, the promised depth equivariance and its generalization gains lose their grounding.

Editorial extensions

If this is right

GrooMeD-NMS removes the train/inference mismatch of non-maximum suppression, giving gradients through the final kept boxes and improving detection on hard, occluded objects at no additional inference cost.
A depth-equivariant backbone built from scale-equivariant steerable convolutions produces more consistent depth estimates, improving cross-dataset transfer and reaching image-only state-of-the-art on KITTI and Waymo.
Large-object detection failures are partly a noise-sensitivity problem: dice loss has lower gradient variance for large objects than L1/L2 depth regression, so bird's-eye-view foreground segmentation with dice loss improves large-object AP on KITTI-360 and nuScenes.
Regressed depth and ground-plane depth have opposite mean-error trends under camera-height change, and averaging them inside the network cancels those trends, giving robust detection at unseen ego heights on CARLA.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

The paper does not combine the four fixes; a natural test is whether a depth-equivariant backbone trained with GrooMeD-NMS and SeaBird's dice supervision compounds the gains.
Because DEVIANT's equivariance lives in the backbone, it should also improve monocular depth estimation and Pseudo-LiDAR pipelines; that extension is not demonstrated here.
CHARM3R's trend cancellation generalizes: any two depth estimators with opposite out-of-distribution biases could be fused the same way, but the thesis only tests regressed versus ground-plane depth.

Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Quick take: this is a genuinely useful thesis on making monocular 3D detectors work under occlusion, dataset shift, large objects, and camera height changes. Four distinct contributions, each benchmarked heavily. The empirical core is in good shape. The theory, especially in the SeaBird chapter, does not hold up as written and should not be called a proof until fixed.

What's new: GrooMeD-NMS is a closed-form differentiable NMS that lets you train with a loss on post-NMS boxes; DEVIANT brings scale-equivariant steerable convolutions to monocular 3D as a first step toward depth equivariance in the projective manifold; SeaBird shows BEV segmentation with dice loss helps large objects; CHARM3R's averaging of regressed and ground-plane depth is a simple, effective trick for camera-height shifts. The CARLA extension with negative height changes is a nice addition. Results on KITTI, Waymo, nuScenes, and KITTI-360 are consistent and mostly published, which tells me the work is reproducible in the usual sense.

Soft spots, in order of importance. The SeaBird theory is the real problem. Lemma 3 and Theorem 2 define sigma_c = max(sigma_m, sqrt(2)/ell * Erf^{-1}(ell^2)). For cars (ell=4 m) and trailers (ell=12 m), ell^2 is 16 and 144, both outside the domain of Erf^{-1}, which is defined only on [-1,1]. So sigma_c as stated is undefined exactly where the theorem is needed. The paper reports sigma_c=0.3 m and 0.1 m, but those numbers cannot come from that expression. The empirical ablation in Table 4.5 still shows dice loss beating MSE, CE, and SmoothL1 on large objects, so the applied pipeline is probably fine; but the 'mathematically proven' claim is unsupported as written. This is an honest error, not a fudge, but it must be fixed. Second, the GrooMeD-NMS derivation replaces the recursive max in Eq. (2.4) with a matrix inverse (Eq. 2.5) and calls it a solution; that's an approximation without a proof of equivalence. It seems to work in practice and the ablations help, but again it is not as rigorous as the phrase 'mathematically differentiable' suggests. Third, DEVIANT's equivariance rests on Corollary 1.1's local-planarity assumption; that is clearly stated, and the cross-dataset gains are real, so I do not consider this a flaw, just a boundary condition. Same for CHARM3R's linear-model theorems: they are toy models, but the averaging trick is empirically validated.

Bottom line: the contribution is the empirical work and the system-level ideas. The theory chapters overreach and should be rewritten as analysis under simplifying assumptions rather than proof. I would bring this to a reading group to discuss, and I would cite the SeaBird and CHARM3R chapters for their practical value. A serious referee should engage with this, but the SeaBird proof issue has to be resolved or reframed before I would trust the theoretical claims.

Referee Report

3 major / 4 minor

Summary. This thesis studies the generalization of monocular 3D object detection (Mono3D) to occlusions, new datasets, large objects, and unseen camera heights. It consists of four self-contained chapters. Chapter 2 introduces GrooMeD-NMS, a grouped, matrix-based differentiable approximation of NMS for end-to-end training, and reports state-of-the-art KITTI results. Chapter 3 introduces DEVIANT, a backbone built from scale-equivariant steerable convolutions, claiming approximate equivariance to ego-depth translations in the projective manifold and reporting improvements on KITTI, Waymo, and cross-dataset depth error. Chapter 4 proposes SeaBird, a BEV-segmentation head trained with dice loss in front of a Mono3D head, together with a theoretical claim that dice loss is more noise-robust for large objects; experiments on KITTI-360 and nuScenes show gains, especially for large objects. Chapter 5 proposes CHARM3R, which fuses regressed depth with ground-plane depth to counteract opposing depth-error trends under camera-height changes, with CARLA experiments supporting the approach. Each chapter includes extensive ablations and comparison tables.

Significance. Assuming the results hold, the thesis makes useful engineering contributions: it demonstrates differentiable NMS training for monocular detection, transfers scale-equivariant convolutions to monocular depth estimation, identifies large-object noise sensitivity and offers a practical mitigation, and proposes a simple height-robust depth-fusion method. The empirical work is broad and consistent: KITTI, KITTI-360, Waymo, nuScenes, and CARLA are used; multiple backbones and detectors are tested; and ablations isolate key design choices. The chapters also disclose limitations rather than claiming full generality. The theoretical scaffolding is weaker than the empirical part. In particular, the SeaBird proof contains a formal domain error in its stated threshold, and the GrooMeD-NMS derivation replaces the recursive max formulation with a matrix inverse without proving or bounding the approximation. These issues do not by themselves invalidate the empirical claims, but they do weaken the advertised mathematical guarantees and should be fixed or explicitly downgraded before publication.

major comments (3)

[Sec. 4.3.2 (Lemma 3 and Theorem 2)] The threshold σ_c = max(σ_m, (√2/ℓ) Erf^{-1}(ℓ^2)) is undefined for the large objects analyzed in this chapter. The inverse error function Erf^{-1} is defined only on [-1, 1], while ℓ = 4 m and ℓ = 12 m give ℓ^2 = 16 and 144, respectively. The reported values σ_c = 0.3 m and 0.1 m for cars and trailers therefore cannot be obtained from the displayed expression. Since this threshold is invoked precisely in the large-object regime that the theorem is meant to explain, the mathematical proof is internally inconsistent as written. The empirical ablation in Tab. 4.5 may still support SeaBird as a method, but the theoretical claim should be repaired, for example by deriving the comparison directly from the Var(ε) expressions, or explicitly downgraded to a heuristic justified empirically.
[Sec. 2.4.1.2, Eqs. (2.4)-(2.5)] The step from the recursive max formulation r ≈ max(s − Pr, 0) to the closed form r ≈ (I + P)^{-1} s is asserted without proof. The inverse expression solves the unrectified equation r = s − Pr, not r = max(s − Pr, 0); the two differ whenever the unrectified r would have negative entries, which can occur because P is nonnegative and lower triangular. No bound on the approximation error, no clipping operation in Eq. (2.5), and no comparison with the recursive form are provided. Because the 'mathematically differentiable NMS' is a central contribution, this gap should be closed, or the inverse expression should be presented explicitly as an additional design approximation rather than as 'the solution' to Eq. (2.4).
[Sec. 3.4, Corollary 1.1 and Table 3.1] Corollary 1.1 establishes a pure scale transformation only for a patch plane that is approximately parallel to the image plane under a pure depth translation. The chapter's abstract and Table 3.1 state that DEVIANT 'is equivariant to the depth translations in the projective manifold' without consistently carrying this qualifier. Since the planarity/parallelism condition is not satisfied by a full three-dimensional object, the proven statement is a local approximation rather than a global equivariance. The chapter acknowledges some of this in the text and limitation paragraph, but the wording should be made consistently 'approximately equivariant', or a quantitative bound on the approximation error in terms of plane orientation and depth translation should be provided.

minor comments (4)

[Sec. 2.4.1.2] The clipping operator defined in Eq. (2.1) is not applied in Eq. (2.5). Please clarify whether rescores from Eq. (2.5) are allowed to be negative and whether the valid threshold v makes this immaterial in practice.
[Sec. 3.5.3 and Eq. (5.4)] The symbol α denotes the DEVIANT downscaling factor in Chapter 3 and the shift coefficient in CHARM3R's Eq. (5.4). A consolidated notation table or a local name change would help readers.
[Tab. 4.5] In the 'Segmentation Loss' ablation, changing dice to SmoothL1, MSE, or CE changes both the loss function and the target distribution; the comparison would be cleaner if the same BEV supervision target and prediction head were used with identical loss weighting wherever possible.
[General] Several leading claims use 'state-of-the-art' where the comparison protocol differs across tables; for example, some tables compare with retrained baselines and others quote official leaderboard entries. Stating the protocol directly under each table would improve reproducibility of the claimed rankings.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: each chapter's core derivation is self-contained and benchmarked externally; the SeaBird proof-domain issue is a correctness gap, not a circular reduction.

full rationale

I walked the claimed derivation chains chapter by chapter. GrooMeD-NMS derives a closed-form matrix NMS from an explicit relaxation of classical/Soft-NMS (Eqs. 2.2-2.7) and is evaluated on KITTI against external baselines, so no fitted quantity is being relabeled as a prediction. DEVIANT's depth-equivariance claim rests on Corollary 1.1, which is derived from Hartley-Zisserman's projective-transformation theorem under stated assumptions, and the scale-equivariant steerable blocks are external building blocks rather than a self-citation carrying the argument; the cross-dataset evaluation is independent. SeaBird's theoretical claim is the only place where the derivation is internally problematic: Lemma 3 and Theorem 2 define sigma_c = max(sigma_m, sqrt(2)/ell * Erf^{-1}(ell^2)), but Erf^{-1} is undefined for inputs greater than 1, and the paper's target objects have ell=4 m and ell=12 m (ell^2=16 and 144), so the reported sigma_c values of 0.3 m and 0.1 m cannot be obtained from the stated expression. This is a mathematical-support gap in the proof, not a circular equivalence: the dice loss is not defined in terms of the theorem's conclusion, and the empirical ablation in Table 4.5 independently compares dice loss against SmoothL1, MSE, and CE on external KITTI-360 data. CHARM3R's Theorems 3 and 4 prove opposing depth-error trends from explicitly stated linear-model and ground-depth assumptions; the simple averaging of the two depth estimates is a fixed design, not a fitted parameter renamed as a prediction, and the CARLA benchmark is external. Self-citations appear as baselines (GrooMeD-NMS, DEVIANT, SeaBird) and as a convention for the additive-noise assumption, but no load-bearing uniqueness theorem or ansatz is imported from the author's own prior work. Overall, no derivation reduces by construction to its inputs, so the circularity score is 0.

Assumptions & free parameters 7 free parameters · 4 assumptions · 0 invented entities

The central contributions rest on several simplifying assumptions. DEVIANT requires local planarity and scale equivalence; SeaBird's theory uses a simplified loss analysis; CHARM3R's trends depend on linear depth models. Hyperparameters for GrooMeD-NMS and DEVIANT are tuned on validation data. No new physical or conceptual entities are introduced.

free parameters (7)

NMS threshold N_t = 0.4
Hyperparameter for GrooMeD-NMS grouping and pruning, tuned on KITTI Val.
valid threshold v = 0.3
Threshold for keeping boxes after NMS, tuned on KITTI Val.
target assignment beta = 0.3
Threshold for assigning positive labels in GrooMeD-NMS, tuned on KITTI Val.
max group size alpha = 100
Maximum group size in GrooMeD-NMS, chosen by hand.
loss weight lambda = 0.05
Weight for the Imagewise AP-Loss in GrooMeD-NMS, chosen by hand.
DEVIANT downscaling factor alpha = 10%
Chosen via ablation on KITTI Val; affects scale equivariance granularity.
DEVIANT number of scales = 3
Selected via ablation on KITTI Val; more scales improve performance on moderate and hard objects.

assumptions (4)

ad hoc to paper NMS rescoring can be approximated by (I+P)^{-1}s instead of the recursive max(s-Pr,0) formulation.
GrooMeD-NMS makes this approximation to obtain a closed-form expression, but it is not rigorously derived from the original NMS algorithm (Section 2.4.1.2, Eq. 2.5).
domain assumption Objects are locally planar and ego depth translation induces a scale transformation in the projective manifold.
This is the core assumption of DEVIANT's depth equivariance, stated in Corollary 1.1 and the surrounding text (Section 3.4).
domain assumption The theoretical analysis in SeaBird assumes a simplified linear regression model, normal noise, constant object length, and perfect foreground segmentation scores.
Lemma 1 and Theorem 2 rely on these simplifications; the authors acknowledge they hold for 'a simplified case' (Sections 4.3.1-4.3.3).
domain assumption The regressed depth model is linear in the pixel y-coordinate, and the ground depth model uses the projected bottom center.
Theorems 3 and 4 in CHARM3R derive trends based on these linear and geometric assumptions (Sections 5.4.1 and 5.4.2).

how reviews work

0 comments

Cite this review

Pith. "Pith review of Generalizing Monocular 3D Object Detection." pith.science (2026). https://pith.science/paper/NGXEHWTU

@misc{pith2026250819593,
  author       = {Pith},
  title        = {Pith review of: Generalizing Monocular 3D Object Detection},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/NGXEHWTU}},
  note         = {Machine review of arXiv:2508.19593}
}

read the original abstract

Monocular 3D object detection (Mono3D) is a fundamental computer vision task that estimates an object's class, 3D position, dimensions, and orientation from a single image. Its applications, including autonomous driving, augmented reality, and robotics, critically rely on accurate 3D environmental understanding. This thesis addresses the challenge of generalizing Mono3D models to diverse scenarios, including occlusions, datasets, object sizes, and camera parameters. To enhance occlusion robustness, we propose a mathematically differentiable NMS (GrooMeD-NMS). To improve generalization to new datasets, we explore depth equivariant (DEVIANT) backbones. We address the issue of large object detection, demonstrating that it's not solely a data imbalance or receptive field problem but also a noise sensitivity issue. To mitigate this, we introduce a segmentation-based approach in bird's-eye view with dice loss (SeaBird). Finally, we mathematically analyze the extrapolation of Mono3D models to unseen camera heights and improve Mono3D generalization in such out-of-distribution settings.

Figures

Figures reproduced from arXiv: 2508.19593 by the authors.

**Figure 2.** [PITH_FULL_IMAGE:figures/full_fig_p025_2.png] view at source ↗

**Figure 2.** [PITH_FULL_IMAGE:figures/full_fig_p029_2.png] view at source ↗

Figures from the paper (15 more)

**Figure 2.** Figure 2 [PITH_FULL_IMAGE:figures/full_fig_p031_2.png]

**Figure 3.** Figure 3 [PITH_FULL_IMAGE:figures/full_fig_p035_3.png]

**Figure 3.** Figure 3 [PITH_FULL_IMAGE:figures/full_fig_p041_3.png]

**Figure 3.** Figure 3 [PITH_FULL_IMAGE:figures/full_fig_p046_3.png]

**Figure 3.** Figure 3 [PITH_FULL_IMAGE:figures/full_fig_p049_3.png]

**Figure 4.** Figure 4 [PITH_FULL_IMAGE:figures/full_fig_p054_4.png]

**Figure 4.** Figure 4 [PITH_FULL_IMAGE:figures/full_fig_p056_4.png]

**Figure 4.** Figure 4 [PITH_FULL_IMAGE:figures/full_fig_p058_4.png]

**Figure 4.** Figure 4 [PITH_FULL_IMAGE:figures/full_fig_p061_4.png]

**Figure 4.** Figure 4 [PITH_FULL_IMAGE:figures/full_fig_p066_4.png]

**Figure 5.** Figure 5 [PITH_FULL_IMAGE:figures/full_fig_p071_5.png]

**Figure 5.** Figure 5 [PITH_FULL_IMAGE:figures/full_fig_p075_5.png]

**Figure 5.** Figure 5 [PITH_FULL_IMAGE:figures/full_fig_p076_5.png]

**Figure 5.** Figure 5 [PITH_FULL_IMAGE:figures/full_fig_p080_5.png]

**Figure 5.** Figure 5 [PITH_FULL_IMAGE:figures/full_fig_p083_5.png]

Review history (2 revisions) →

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

300 extracted references · 59 canonical work pages

[1]

http://www.cvlibs.net/datasets/kitti/eval_object.php? obj_benchmark=3d

The KITTI Vision Benchmark Suite. http://www.cvlibs.net/datasets/kitti/eval_object.php? obj_benchmark=3d. Accessed: 2022-07-03. 18, 35

2022
[2]

Augmentedrealitymeetscomputervision: Efficientdatagenerationforurbandrivingscenes

Hassan Alhaĳa, Siva Mustikovela, Lars Mescheder, Andreas Geiger, and Carsten Rother. Augmentedrealitymeetscomputervision: Efficientdatagenerationforurbandrivingscenes. ĲCV, 2018. 1, 6, 25, 44, 61

2018
[3]

Learningdetectionwithdiverseproposals

SamanehAzadi,JiashiFeng,andTrevorDarrell. Learningdetectionwithdiverseproposals. InCVPR, 2017. 9, 16, 17

2017
[4]

VD3D: Taming large video diffusion transformers for 3D camera control.arXiv preprint arXiv:2407.12781, 2024

Sherwin Bahmani, Ivan Skorokhodov, Aliaksandr Siarohin, Willi Menapace, Guocheng Qian, Michael Vasilkovsky, Hsin-Ying Lee, Chaoyang Wang, Jiaxu Zou, Andrea Tagliasac- chi, David Lindell, and Sergey Tulyakov. VD3D: Taming large video diffusion transformers for 3D camera control.arXiv preprint arXiv:2407.12781, 2024. 63, 65

arXiv 2024
[5]

MonoFENet: Monocular3D object detection with feature enhancement networks.IEEE Transactions on Image Processing, 2019

Wentao Bao, Bin Xu, and Zhenzhong Chen. MonoFENet: Monocular3D object detection with feature enhancement networks.IEEE Transactions on Image Processing, 2019. 9

2019
[6]

SemanticKITTI: A dataset for semantic scene understanding of LiDAR sequences

Jens Behley, Martin Garbade, Andres Milioto, Jan Quenzel, Sven Behnke, Cyrill Stachniss, and Jurgen Gall. SemanticKITTI: A dataset for semantic scene understanding of LiDAR sequences. InICCV, 2019. 54

2019
[7]

Monocular differentiable rendering for self-supervised3D object detection

DenizBeker,HiroharuKato,MihaiAdrianMorariu,TakahiroAndo,ToruMatsuoka,Wadim Kehl, and Adrien Gaidon. Monocular differentiable rendering for self-supervised3D object detection. InECCV, 2020. 20

2020
[8]

Learning with differentiable perturbed optimizers

Quentin Berthet, Mathieu Blondel, Olivier Teboul, Marco Cuturi, Jean-Philippe Vert, and Francis Bach. Learning with differentiable perturbed optimizers. InNeurIPS, 2020. 11

2020

Show all 300 references

[9]

An inequality for Mill’s ratio.The Annals of Mathematical Statistics,

Zygmunt Birnbaum. An inequality for Mill’s ratio.The Annals of Mathematical Statistics,
[10]

Fast differentiable sorting and ranking

Mathieu Blondel, Olivier Teboul, Quentin Berthet, and Josip Djolonga. Fast differentiable sorting and ranking. InICML, 2020. 11, 12

2020
[11]

YOLOv4: Optimal speed and accuracy of object detection.arXiv preprint arXiv:2004.10934, 2020

Alexey Bochkovskiy, Chien-Yao Wang, and Hong-Yuan Mark Liao. YOLOv4: Optimal speed and accuracy of object detection.arXiv preprint arXiv:2004.10934, 2020. 26

2004 arXiv
[12]

Soft-NMS–improving object detection with one line of code

Navaneeth Bodla, Bharat Singh, Rama Chellappa, and Larry Davis. Soft-NMS–improving object detection with one line of code. InICCV, 2017. 8, 9, 10, 11, 15, 16, 17, 21, 23, 108, 109, 110

2017
[13]

Soft-NMSimplementa- tion

NavaneethBodla,BharatSingh,RamaChellappa,andLarryDavis. Soft-NMSimplementa- tion. https://github.com/bharatsingh430/soft-nms/blob/master/lib/nms/cpu_nms.pyx#L98,
[14]

Omni3D: A large benchmark and model for3D object detection in the wild

Garrick Brazil, Abhinav Kumar, Julian Straub, Nikhila Ravi, Justin Johnson, and Georgia Gkioxari. Omni3D: A large benchmark and model for3D object detection in the wild. In CVPR, 2023. 1, 44, 55, 56, 57, 61, 65, 78

2023
[15]

M3D-RPN: Monocular3D region proposal network for object detection

Garrick Brazil and Xiaoming Liu. M3D-RPN: Monocular3D region proposal network for object detection. InICCV, 2019. 6, 9, 16, 18, 19, 20, 21, 22, 23, 25, 26, 29, 37, 42, 48, 65, 108, 110, 121, 128, 129

2019
[16]

Pedestriandetectionwithautoregressivenetworkphases

GarrickBrazilandXiaomingLiu. Pedestriandetectionwithautoregressivenetworkphases. InCVPR, 2019. 9

2019
[17]

Kinematic3D object detectioninmonocularvideo

Garrick Brazil, Gerard Pons-Moll, Xiaoming Liu, and Bernt Schiele. Kinematic3D object detectioninmonocularvideo. In ECCV,2020. 6,7,9,16,18,19,20,21,22,23,25,29,35, 36, 48, 65, 74, 107, 108, 109, 110, 111, 112, 113, 117, 121, 131

2020
[18]

Illuminating pedestrians via simultaneous detection & segmentation

Garrick Brazil, Xi Yin, and Xiaoming Liu. Illuminating pedestrians via simultaneous detection & segmentation. InICCV, 2017. 9

2017
[19]

Convolution from first principles

Michael Bronstein. Convolution from first principles. https://towardsdatascience.com/ deriving-convolution-from-first-principles-4ff124888028. Accessed: 2021-08-13. 25, 28, 29, 114

2021
[20]

Geometricdeeplearning: Grids, groups, graphs, geodesics, and gauges.arXiv preprint arXiv:2104.13478, 2021

MichaelBronstein,JoanBruna,TacoCohen,andPetarVeličković. Geometricdeeplearning: Grids, groups, graphs, geodesics, and gauges.arXiv preprint arXiv:2104.13478, 2021. 28, 29, 114

2021 arXiv
[21]

The non-existence of general-case view-invariants

Brian Burns, Richard Weiss, and Edward Riseman. The non-existence of general-case view-invariants. InGeometric invariance in computer vision. 1992. 29, 30, 63, 114, 115

1992
[22]

nuScenes: A multimodal dataset for autonomous driving

Holger Caesar, Varun Bankiti, Alex Lang, Sourabh Vora, Venice Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, and Oscar Beĳbom. nuScenes: A multimodal dataset for autonomous driving. InCVPR, 2020. 34, 35, 37, 46, 53, 54, 60, 73, 128, 149, 164

2020
[23]

2 die when tesla crashes into parked tractor-trailer in florida

Brittany Caldwell. 2 die when tesla crashes into parked tractor-trailer in florida. https: //www.wftv.com/news/local/2-die-when-tesla-crashes-into-parked-tractor-trailer-florida/ KJGMHHYTQZA2HNAHWL2OFSVIPM/, 2022. Accessed: 2023-11-06. 2, 45

2022
[24]

End-to-endobjectdetectionwithtransformers

Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, andSergeyZagoruyko. End-to-endobjectdetectionwithtransformers. In ECCV,2020. 48, 65

2020
[25]

Deep MANTA: A coarse-to-fine many-task network for joint2D and 3D vehi- cle analysis from monocular image

Florian Chabot, Mohamed Chaouch, Jaonary Rabarisoa, Céline Teuliere, and Thierry Chateau. Deep MANTA: A coarse-to-fine many-task network for joint2D and 3D vehi- cle analysis from monocular image. InCVPR, 2017. 29, 48, 65

2017
[26]

Unified domain generalization and adaptation for multi-view3D 81 object detection

GyusamChang,JiwonLee,DonghyunKim,JinkyuKim,DongwookLee,DaehyunJi,Sujin Jang, and Sangpil Kim. Unified domain generalization and adaptation for multi-view3D 81 object detection. InNeurIPS, 2024. 65

2024
[27]

CMDA: Cross-modal and domain adversarial adaptation for LiDAR-based3D object detection

Gyusam Chang, Wonseok Roh, Sujin Jang, Dongwook Lee, Daehyun Ji, Gyeongrok Oh, Jinsun Park, Jinkyu Kim, and Sangpil Kim. CMDA: Cross-modal and domain adversarial adaptation for LiDAR-based3D object detection. InAAAI, 2024. 65

2024
[28]

RecurrentBEV: A long-term temporal fusion framework for multi-view3D detection

Ming Chang, Xishan Zhang, Rui Zhang, Zhipeng Zhao, Guanhua He, and Shaoli Liu. RecurrentBEV: A long-term temporal fusion framework for multi-view3D detection. In ECCV, 2024. 65

2024
[29]

Viewpoint equivariance for multi-view3D object detection

Dian Chen, Jie Li, Vitor Guizilini, Rares Andrei Ambrus, and Adrien Gaidon. Viewpoint equivariance for multi-view3D object detection. InCVPR, 2023. 48, 59, 65

2023
[30]

AP-Lossforaccurate one-stage object detection.TPAMI, 2020

KeanChen,WeiyaoLin,JianguoLi,JohnSee,JiWang,andJunniZou. AP-Lossforaccurate one-stage object detection.TPAMI, 2020. 17, 19

2020
[31]

Monocular 3D object detection for autonomous driving

Xiaozhi Chen, Kaustav Kundu, Ziyu Zhang, Huimin Ma, Sanja Fidler, and Raquel Urtasun. Monocular 3D object detection for autonomous driving. InCVPR, 2016. 9, 29, 47, 64

2016
[32]

InNeurIPS,

Xiaozhi Chen, Kaustav Kundu, Yukun Zhu, Andrew Berneshawi, Huimin Ma, Sanja Fidler, and Raquel Urtasun.3D object proposals for accurate object class detection. InNeurIPS,
[33]

Multi-view3D object detection network for autonomous driving

Xiaozhi Chen, Huimin Ma, Ji Wan, Bo Li, and Tian Xia. Multi-view3D object detection network for autonomous driving. InCVPR, 2017. 6, 9

2017
[34]

DSGN: Deep stereo geometry network for 3D object detection

Yilun Chen, Shu Liu, Xiaoyong Shen, and Jiaya Jia. DSGN: Deep stereo geometry network for 3D object detection. InCVPR, 2020. 1, 47, 64

2020
[35]

MonoPair: Monocular 3D object detection using pairwise spatial relationships

Yongjian Chen, Lei Tai, Kai Sun, and Mingyang Li. MonoPair: Monocular 3D object detection using pairwise spatial relationships. InCVPR, 2020. 6, 9, 19, 20, 21, 23, 25, 29, 36, 48, 65

2020
[36]

Learning high-resolution vector representation from multi-camera images for 3D object detection

Zhili Chen, Shuangjie Xu, Maosheng Ye, Zian Qian, Xiaoyi Zou, Dit-Yan Yeung, and Qifeng Chen. Learning high-resolution vector representation from multi-camera images for 3D object detection. InECCV, 2024. 65

2024
[37]

NEAT: Neural attention fields for end-to-end autonomous driving

Kashyap Chitta, Aditya Prakash, and Andreas Geiger. NEAT: Neural attention fields for end-to-end autonomous driving. InICCV, 2021. 48

2021
[38]

Depth-discriminativemetriclearningfor monocular 3D object detection

WonhyeokChoi, MingyuShin, andSunghoonIm. Depth-discriminativemetriclearningfor monocular 3D object detection. InNeurIPS, 2023. 48, 65

2023
[39]

MonoDistill: Learningspatialfeaturesformonocular 3Dobjectdetection

Zhiyu Chong, Xinzhu Ma, Hong Zhang, Yuxin Yue, Haojie Li, Zhihui Wang, and Wanli Ouyang. MonoDistill: Learningspatialfeaturesformonocular 3Dobjectdetection. In ICLR,
[40]

OA-BEV: Bringing object awareness to bird’s-eye-view representation for multi- camera 3D object detection.arXiv preprint arXiv:2301.05711, 2023

Xiaomeng Chu, Jiajun Deng, Yuan Zhao, Jianmin Ji, Yu Zhang, Houqiang Li, and Yanyong 82 Zhang. OA-BEV: Bringing object awareness to bird’s-eye-view representation for multi- camera 3D object detection.arXiv preprint arXiv:2301.05711, 2023. 48, 65

2023 arXiv
[41]

Spherical CNNs

Taco Cohen, Mario Geiger, Jonas Köhler, and Max Welling. Spherical CNNs. InICLR,
[42]

Learning the irreducible representations of commutative lie groups

Taco Cohen and Max Welling. Learning the irreducible representations of commutative lie groups. InICML, 2014. 28

2014
[43]

Group equivariant convolutional networks

Taco Cohen and Max Welling. Group equivariant convolutional networks. InICML, 2016. 28, 63, 114

2016
[44]

MMDetection3D: OpenMMLab next-generation platform forgeneral 3Dobjectdetection

MMDetection3D Contributors. MMDetection3D: OpenMMLab next-generation platform forgeneral 3Dobjectdetection. https://github.com/open-mmlab/mmdetection3d,2020. 149

2020
[45]

Multi-tasklearningwithdeepneuralnetworks: Asurvey

MichaelCrawshaw. Multi-tasklearningwithdeepneuralnetworks: Asurvey. arXivpreprint arXiv:2009.09796, 2020. 46

2009 arXiv
[46]

Differentiableranksandsortingusing optimal transport

MarcoCuturi,OlivierTeboul,andJean-PhilippeVert. Differentiableranksandsortingusing optimal transport. InNeurIPS, 2019. 11

2019
[47]

Histograms of oriented gradients for human detection

Navneet Dalal and Bill Triggs. Histograms of oriented gradients for human detection. In CVPR, 2005. 9

2005
[48]

ImageNet: A large-scale hierarchical image database

Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. ImageNet: A large-scale hierarchical image database. InCVPR, 2009. 124

2009
[49]

Discriminative models for multi- class object layout.ĲCV, 2011

Chaitanya Desai, Deva Ramanan, and Charless Fowlkes. Discriminative models for multi- class object layout.ĲCV, 2011. 9, 16, 17

2011
[50]

Exploiting cyclic symmetry in convolutional neural networks

Sander Dieleman, Jeffrey De Fauw, and Koray Kavukcuoglu. Exploiting cyclic symmetry in convolutional neural networks. InICML, 2016. 28, 114

2016
[51]

Howdoneuralnetworksseedepthinsingleimages? In ICCV, 2019

TomvanDĳkandGuidodeCroon. Howdoneuralnetworksseedepthinsingleimages? In ICCV, 2019. 71, 161

2019
[52]

Learning depth-guided convolutions for monocular3D object detection

Mingyu Ding, Yuqi Huo, Hongwei Yi, Zhe Wang, Jianping Shi, Zhiwu Lu, and Ping Luo. Learning depth-guided convolutions for monocular3D object detection. In CVPR Workshops, 2020. 9, 19, 20, 29, 38, 39, 121

2020
[53]

SpatialDETR:Robustscalabletransformer-based 3Dobjectdetectionfrom multi-view camera images with global cross-sensor attention

Simon Doll, Richard Schulz, Lukas Schneider, Viviane Benzin, Markus Enzweiler, and HendrikLensch. SpatialDETR:Robustscalabletransformer-based 3Dobjectdetectionfrom multi-view camera images with global cross-sensor attention. InECCV, 2022. 59

2022
[54]

Benchmarking robustness of3D object detection to common corruptions

Yinpeng Dong, Caixin Kang, Jinlai Zhang, Zĳian Zhu, Yikai Wang, Xiao Yang, Hang Su, Xingxing Wei, and Jun Zhu. Benchmarking robustness of3D object detection to common corruptions. InCVPR, 2023. 1, 44 83

2023
[55]

Animageisworth16x16words: Transformersforimage recognition at scale

Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, JakobUszkoreit,andNeilHoulsby. Animageisworth16x16words: Transformersforimage recognition at scale. InI...

2021
[56]

Polar transformer networks

Carlos Esteves, Christine Allen-Blanchette, Xiaowei Zhou, and Kostas Daniilidis. Polar transformer networks. InICLR, 2018. 28

2018
[57]

CAM-Convs: Camera-aware multi-scale convolutions for single-view depth

Jose Facil, Benjamin Ummenhofer, Huizhong Zhou, Luis Montesano, Thomas Brox, and Javier Civera. CAM-Convs: Camera-aware multi-scale convolutions for single-view depth. InCVPR, 2019. 74

2019
[58]

Fully sparse3D object detection

Lue Fan, Feng Wang, Naiyan Wang, and Zhao Zhang. Fully sparse3D object detection. In NeurIPS, 2022. 48

2022
[59]

AEDet: Azimuth- invariant multi-view3D object detection.arXiv preprint arXiv:2211.12501, 2022

Chengjian Feng, Zequn Jie, Yujie Zhong, Xiangxiang Chu, and Lin Ma. AEDet: Azimuth- invariant multi-view3D object detection.arXiv preprint arXiv:2211.12501, 2022. 48

2022 arXiv
[60]

A tesla driver was killed after smashing into a firetruck on a california highway

Roshan Fernandez. A tesla driver was killed after smashing into a firetruck on a california highway. https://www.npr.org/2023/02/20/1158367204/ tesla-driver-killed-california-firetruck-nhtsa, 2023. Accessed: 2023-11-06. 2, 45

2023
[61]

3D object detection and viewpoint estimation with a deformable3D cuboid model

Sanja Fidler, Sven Dickinson, and Raquel Urtasun. 3D object detection and viewpoint estimation with a deformable3D cuboid model. InNeurIPS, 2012. 9, 29

2012
[62]

The design and use of steerable filters.TPAMI,

William Freeman and Edward Adelson. The design and use of steerable filters.TPAMI,
[63]

Training or architecture? how to incorporate invariance in neural networks.arXiv preprint arXiv:2106.10044, 2021

KanchanaGandikota,JonasGeiping,ZorahLähner,AdamCzapliński,andMichaelMoeller. Training or architecture? how to incorporate invariance in neural networks.arXiv preprint arXiv:2106.10044, 2021. 27, 38, 114

2021 arXiv
[64]

Hyperbolic neural net- works

Octavian-Eugen Ganea, Gary Bécigneul, and Thomas Hofmann. Hyperbolic neural net- works. InNeurIPS, 2017. 26, 28

2017
[65]

3D-LaneNet: end-to-end 3D multiple lane detection

NoaGarnett,RafiCohen,TomerPe’er,RoeeLahav,andDanLevi. 3D-LaneNet: end-to-end 3D multiple lane detection. InICCV, 2019. 66, 67

2019
[66]

Vision meets robotics: The KITTI dataset.ĲRR, 2013

Andreas Geiger, Philip Lenz, Christoph Stiller, and Raquel Urtasun. Vision meets robotics: The KITTI dataset.ĲRR, 2013. 112, 134

2013
[67]

Are we ready for autonomous driving? the KITTI vision benchmark suite

Andreas Geiger, Philip Lenz, and Raquel Urtasun. Are we ready for autonomous driving? the KITTI vision benchmark suite. InCVPR, 2012. 18, 20, 34, 35, 53, 54, 74, 146

2012
[68]

Scalesteerablefiltersforlocallyscale-invariantconvolu- tional neural networks

RohanGhoshandAnupamGupta. Scalesteerablefiltersforlocallyscale-invariantconvolu- tional neural networks. InICML Workshops, 2019. 27, 28, 32, 122

2019
[69]

Fast R-CNN

Ross Girshick. Fast R-CNN. InICCV, 2015. 8, 9, 55 84

2015
[70]

Rich feature hierarchies for accurate object detection and semantic segmentation

Ross Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik. Rich feature hierarchies for accurate object detection and semantic segmentation. InCVPR, 2014. 8

2014
[71]

Matrix computations

Gene Golub and Charles Loan. Matrix computations. 2013. 14

2013
[72]

Bird’s-eye-viewpanopticsegmentationusingmonocular frontal view images.RAL, 2022

NikhilGosalaandAbhinavValada. Bird’s-eye-viewpanopticsegmentationusingmonocular frontal view images.RAL, 2022. 48, 54, 55, 57, 148, 150, 152

2022
[73]

Gen-lanenet: A generalized and scalable approach for3D lane detection

Yuliang Guo, Guang Chen, Peitao Zhao, Weide Zhang, Jinghao Miao, Jingao Wang, and Tae Eun Choe. Gen-lanenet: A generalized and scalable approach for3D lane detection. In ECCV, 2020. 66, 67

2020
[74]

Simple- BEV: What really matters for multi-sensor BEV perception? InCoRL, 2022

Adam Harley, Zhaoyuan Fang, Jie Li, Rares Ambrus, and Katerina Fragkiadaki. Simple- BEV: What really matters for multi-sensor BEV perception? InCoRL, 2022. 48

2022
[75]

A combined corner and edge detector

Christopher Harris and Mike Stephens. A combined corner and edge detector. InAlvey vision conference, 1988. 9

1988
[76]

Cam- bridge university press, 2003

Richard Hartley and Andrew Zisserman.Multiple view geometry in computer vision. Cam- bridge university press, 2003. 27, 29, 30, 31, 63, 66, 116, 117

2003
[77]

Deepresiduallearningforimage recognition

KaimingHe,XiangyuZhang,ShaoqingRen,andJianSun. Deepresiduallearningforimage recognition. InCVPR, 2016. 150

2016
[78]

End-to-end training of object class detectors for mean average precision

Paul Henderson and Vittorio Ferrari. End-to-end training of object class detectors for mean average precision. InACCV, 2016. 9, 18, 24

2016
[79]

Warped convolutions: Efficient invariance to spatial transformations

Joao Henriques and Andrea Vedaldi. Warped convolutions: Efficient invariance to spatial transformations. InICML, 2017. 28

2017
[80]

A convnet for non-maximum suppres- sion

Jan Hosang, Rodrigo Benenson, and Bernt Schiele. A convnet for non-maximum suppres- sion. InGCPR, 2016. 7, 9, 16, 17, 18, 24

2016
[81]

Learning non-maximum suppression

Jan Hosang, Rodrigo Benenson, and Bernt Schiele. Learning non-maximum suppression. InCVPR, 2017. 7, 9, 16, 17, 18, 24

2017
[82]

OPEN:Object-wisepositionembeddingformulti-view 3Dobjectdetection

Jinghua Hou, Tong Wang, Xiaoqing Ye, Zhe Liu, Xiao Tan, Errui Ding, Jingdong Wang, andXiangBai. OPEN:Object-wisepositionembeddingformulti-view 3Dobjectdetection. InECCV, 2024. 65

2024
[83]

FIERY: future instance prediction in bird’s-eye view from surround monocular cameras

AnthonyHu,ZakMurez,NikhilMohan,SofíaDudas,JeffreyHawke,VĳayBadrinarayanan, Roberto Cipolla, and Alex Kendall. FIERY: future instance prediction in bird’s-eye view from surround monocular cameras. InICCV, 2021. 48, 50

2021
[84]

Investigating theimpactofmulti-LiDARplacementonobjectdetectionforautonomousdriving

Hanjiang Hu, Zuxin Liu, Sharad Chitlangia, Akhil Agnihotri, and Ding Zhao. Investigating theimpactofmulti-LiDARplacementonobjectdetectionforautonomousdriving. In CVPR,
[85]

What you see is what you get: Exploiting visibility for3D object detection

Peiyun Hu, Jason Ziglar, David Held, and Deva Ramanan. What you see is what you get: Exploiting visibility for3D object detection. InCVPR, 2020. 9

2020
[86]

Densely connected convolutional networks

Gao Huang, Zhuang Liu, Laurens Maaten, and Kilian Weinberger. Densely connected convolutional networks. InCVPR, 2017. 18

2017
[87]

BEVDet4D: Exploit temporal cues in multi-camera3D object detection.arXiv preprint arXiv:2203.17054, 2022

Junjie Huang and Guan Huang. BEVDet4D: Exploit temporal cues in multi-camera3D object detection.arXiv preprint arXiv:2203.17054, 2022. 55, 155

2022 arXiv
[88]

BEVDet: High-performance multi-camera 3D object detection in bird-eye-view

Junjie Huang, Guan Huang, Zheng Zhu, Yun Ye, and Dalong Du. BEVDet: High-performance multi-camera 3D object detection in bird-eye-view. arXiv preprint arXiv:2112.11790, 2021. 48, 65, 155

2021 arXiv
[89]

InCVPR, 2022

Kuan-ChihHuang,Tsung-HanWu,Hung-TingSu,andWinstonHsu.MonoDTR:Monocular 3D object detection with depth-aware transformer. InCVPR, 2022. 47, 65

2022
[90]

EPNet: Enhancing point features with image semantics for3D object detection

Tengteng Huang, Zhe Liu, Xiwu Chen, and Xiang Bai. EPNet: Enhancing point features with image semantics for3D object detection. InECCV, 2020. 6, 7, 9, 23

2020
[91]

STXD:Structural andtemporalcross-modaldistillationformulti-view 3Dobjectdetection

SujinJang,DaeUngJo,SungJuHwang,DongwookLee,andDaehyunJi. STXD:Structural andtemporalcross-modaldistillationformulti-view 3Dobjectdetection. In NeurIPS,2023. 59

2023
[92]

Scale-invariant scale-channel networks: Deep networks that generalise to previously unseen scales.ĲCV, 2021

Ylva Jansson and Tony Lindeberg. Scale-invariant scale-channel networks: Deep networks that generalise to previously unseen scales.ĲCV, 2021. 27, 28, 32

2021
[93]

Enhancing 3Dobjectdetectionwith 2D detection-guided query anchors

HaoxuanyeJi,PengpengLiang,andErkangCheng. Enhancing 3Dobjectdetectionwith 2D detection-guided query anchors. InCVPR, 2024. 65

2024
[94]

MonoUNI:Aunifiedvehicleandinfrastructure-side monocular 3D object detection network with sufficient depth clues

JinrangJia,ZhenjiaLi,andYifengShi. MonoUNI:Aunifiedvehicleandinfrastructure-side monocular 3D object detection network with sufficient depth clues. InNeurIPS, 2023. 1, 44, 65

2023
[95]

Polarformer: Multi-camera 3D object detection with polar transformers

Yanqin Jiang, Li Zhang, Zhenwei Miao, Xiatian Zhu, Jin Gao, Weiming Hu, and Yu-Gang Jiang. Polarformer: Multi-camera 3D object detection with polar transformers. InAAAI,
[96]

FSD-BEV:Foregroundself-distillationformulti-view 3Dobjectdetection

Zheng Jiang, Jinqing Zhang, Yanan Zhang, Qingjie Liu, Zhenghui Hu, Baohui Wang, and YunhongWang. FSD-BEV:Foregroundself-distillationformulti-view 3Dobjectdetection. InECCV, 2024. 65

2024
[97]

Physical symmetry enhanced neural networks

Li Jing. Physical symmetry enhanced neural networks. PhD thesis, Massachusetts Institute of Technology, 2020. 28

2020
[98]

Locally scale-invariant convolu- tional neural networks

Angjoo Kanazawa, Abhishek Sharma, and David Jacobs. Locally scale-invariant convolu- tional neural networks. InNeurIPS Workshops, 2014. 28

2014
[99]

Probabilistic anchor assignment with IoU prediction for object 86 detection

Kang Kim and Hee Lee. Probabilistic anchor assignment with IoU prediction for object 86 detection. InECCV, 2020. 19, 24

2020
[100]

La- belDistill: Label-guided cross-modal knowledge distillation for camera-based3D object detection

Sanmin Kim, Youngseok Kim, Sihwan Hwang, Hyeonjun Jeong, and Dongsuk Kum. La- belDistill: Label-guided cross-modal knowledge distillation for camera-based3D object detection. InECCV, 2024. 65

2024
[101]

PredicttoDetect: Prediction- guided 3D object detection using sequential images

SanminKim,YoungseokKim,In-JaeLee,andDongsukKum. PredicttoDetect: Prediction- guided 3D object detection using sequential images. InICCV, 2023. 59, 155

2023
[102]

Adam: A method for stochastic optimization

Diederik Kingma and Jimmy Ba. Adam: A method for stochastic optimization. InICLR,
[103]

Last layer re-training is sufficient for robustness to spurious correlations

Polina Kirichenko, Pavel Izmailov, and Andrew Gordon Wilson. Last layer re-training is sufficient for robustness to spurious correlations. InICLR, 2022. 64

2022
[104]

TowardsviewpointrobustnessinBird’sEye View segmentation

Tzofi Klinghoffer, Jonah Philion, Wenzheng Chen, Or Litany, Zan Gojcic, Jungseock Joo, RameshRaskar,SanjaFidler,andJoseAlvarez. TowardsviewpointrobustnessinBird’sEye View segmentation. InICCV, 2023. 1, 44, 62, 63, 65, 73, 76, 161, 162

2023
[105]

X3KD: Knowledge distillation across modalities, tasks and stages for multi-camera3D object detection

Marvin Klingner, Shubhankar Borse, Varun Ravi Kumar, Behnaz Rezaei, Venkatraman Narayanan, Senthil Yogamani, and Fatih Porikli. X3KD: Knowledge distillation across modalities, tasks and stages for multi-camera3D object detection. InCVPR, 2023. 48, 59

2023
[106]

Analyzing differentiable fuzzy logic operators

Emile Krieken, Erman Acar, and Frank Harmelen. Analyzing differentiable fuzzy logic operators. arXiv preprint arXiv:2002.06100, 2020. 12, 106

2002 arXiv
[107]

Monocular3D object detection leveraging accurate proposals and shape reconstruction

Jason Ku, Alex Pon, and Steven Waslander. Monocular3D object detection leveraging accurate proposals and shape reconstruction. InCVPR, 2019. 19

2019
[108]

DEVIANT:DepthEquivariantNetworkformonocular 3Dobjectdetection

Abhinav Kumar, Garrick Brazil, Enrique Corona, Armin Parchami, and Xiaoming Liu. DEVIANT:DepthEquivariantNetworkformonocular 3Dobjectdetection. In ECCV,2022. 1, 44, 48, 50, 53, 54, 55, 56, 57, 61, 63, 65, 70, 73, 74, 75, 76, 146, 147, 152, 153, 156, 162, 163, 164

2022
[109]

GrooMeD-NMS: Grouped mathemat- ically differentiable NMS for monocular3D object detection

Abhinav Kumar, Garrick Brazil, and Xiaoming Liu. GrooMeD-NMS: Grouped mathemat- ically differentiable NMS for monocular3D object detection. InCVPR, 2021. 29, 33, 35, 36, 37, 48, 55, 56, 57, 65, 74, 126, 128, 132, 134, 151

2021
[110]

SeaBird: Seg- mentation in bird’s view with dice loss improves monocular3D detection of large objects

Abhinav Kumar, Yuliang Guo, Xinyu Huang, Liu Ren, and Xiaoming Liu. SeaBird: Seg- mentation in bird’s view with dice loss improves monocular3D detection of large objects. InCVPR, 2024. 1, 61, 65, 70, 72, 75, 162

2024
[111]

LUVLi face alignment: Estimating landmarks’ location, uncertainty, and visibility likelihood

Abhinav Kumar, Tim Marks, Wenxuan Mou, Ye Wang, Michael Jones, Anoop Cherian, Toshiaki Koike-Akino, Xiaoming Liu, and Chen Feng. LUVLi face alignment: Estimating landmarks’ location, uncertainty, and visibility likelihood. InCVPR, 2020. 7, 29, 48, 65

2020
[112]

Estimation of bandlimited signals from the signs of noisy samples

Animesh Kumar and Vinod Prabhakaran. Estimation of bandlimited signals from the signs of noisy samples. InICASSP, 2013. 13, 33, 107 87

2013
[113]

A simpler approach to obtaining anO(1/𝑡) convergenceratefortheprojectedstochasticsubgradientmethod

Simon Lacoste-Julien, Mark Schmidt, and Francis Bach. A simpler approach to obtaining anO(1/𝑡) convergenceratefortheprojectedstochasticsubgradientmethod. arXivpreprint arXiv:1212.2002, 2012. 49, 138, 141

2002 arXiv
[114]

MSeg: A composite dataset for multi-domain semantic segmentation

John Lambert, Zhuang Liu, Ozan Sener, James Hays, and Vladlen Koltun. MSeg: A composite dataset for multi-domain semantic segmentation. InCVPR, 2020. 130

2020
[115]

Gradient-based learning applied to document recognition.Proceedings of the IEEE, 1998

Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner. Gradient-based learning applied to document recognition.Proceedings of the IEEE, 1998. 28, 29

1998
[116]

Individualness and determinantal point processes for pedestrian detection

Donghoon Lee, Geonho Cha, Ming-Hsuan Yang, and Songhwai Oh. Individualness and determinantal point processes for pedestrian detection. InECCV, 2016. 9, 15, 17

2016
[117]

BAAM: Monocular 3D pose and shape reconstruction with bi-contextual attention module and attention-guided modeling

Hyo-Jun Lee, Hanul Kim, Su-Min Choi, Seong-Gyun Jeong, and Yeong Koh. BAAM: Monocular 3D pose and shape reconstruction with bi-contextual attention module and attention-guided modeling. InCVPR, 2023. 48, 65

2023
[118]

From big to small: Multi-scale local planar guidance for monocular depth estimation.arXiv preprint arXiv:1907.10326, 2019

Jin Lee, Myung Han, Dong Ko, and Il Suh. From big to small: Multi-scale local planar guidance for monocular depth estimation.arXiv preprint arXiv:1907.10326, 2019. 130

1907 arXiv
[119]

Diversify and disambiguate: Learning from underspecified data

Yoonho Lee, Huaxiu Yao, and Chelsea Finn. Diversify and disambiguate: Learning from underspecified data. InICLR, 2022. 64

2022
[120]

Learning hand-eyecoordinationforroboticgraspingwithdeeplearningandlarge-scaledatacollection

Sergey Levine, Peter Pastor, Alex Krizhevsky, Julian Ibarz, and Deirdre Quillen. Learning hand-eyecoordinationforroboticgraspingwithdeeplearningandlarge-scaledatacollection. ĲRR, 2018. 6

2018
[121]

GS3D:Anefficient 3D object detection framework for autonomous driving

BuyuLi, WanliOuyang, LuSheng, XingyuZeng, andXiaogangWang. GS3D:Anefficient 3D object detection framework for autonomous driving. InCVPR, 2019. 6, 19

2019
[122]

Stereo R-CNN based3D object detection for autonomous driving

Peiliang Li, Xiaozhi Chen, and Shaojie Shen. Stereo R-CNN based3D object detection for autonomous driving. InCVPR, 2019. 9

2019
[123]

RTM3D: Real-time monocular3D detection from object keypoints for autonomous driving

Peixuan Li, Huaici Zhao, Pengfei Liu, and Feidao Cao. RTM3D: Real-time monocular3D detection from object keypoints for autonomous driving. InECCV, 2020. 6, 9, 19, 25, 29

2020
[124]

Augmenting neural networks with first-order logic

Tao Li and Vivek Srikumar. Augmenting neural networks with first-order logic. InACL,
[125]

BEVStereo: Enhancingdepthestimationinmulti-view 3Dobjectdetectionwithdynamictemporalstereo

Yinhao Li, Han Bao, Zheng Ge, Jinrong Yang, Jianjian Sun, and Zeming Li. BEVStereo: Enhancingdepthestimationinmulti-view 3Dobjectdetectionwithdynamictemporalstereo. InAAAI, 2023. 48, 59, 65

2023
[126]

Unifying voxel- based representation with transformer for3D object detection

Yanwei Li, Yilun Chen, Xiaojuan Qi, Zeming Li, Jian Sun, and Jiaya Jia. Unifying voxel- based representation with transformer for3D object detection. InNeurIPS, 2022. 59

2022
[127]

88 InAAAI, 2023

Yinhao Li, Zheng Ge, Guanyi Yu, Jinrong Yang, Zengran Wang, Yukang Shi, Jianjian Sun, andZemingLi.BEVDepth: Acquisitionofreliabledepthformulti-view 3Dobjectdetection. 88 InAAAI, 2023. 59, 155

2023
[128]

Fast-BEV: A fast and strong bird’s-eye view perception baseline

Yangguang Li, Bin Huang, Zeren Chen, Yufeng Cui, Feng Liang, Mingzhu Shen, Fenggang Liu, Enze Xie, Lu Sheng, Wanli Ouyang, and Jing Shao. Fast-BEV: A fast and strong bird’s-eye view perception baseline. InNeurIPS Workshops, 2023. 48, 65

2023
[129]

UniDrive: Towards universal driving perception across camera configurations.arXiv preprint arXiv:2410.13864, 2024

Ye Li, Wenzhao Zheng, Xiaonan Huang, and Kurt Keutzer. UniDrive: Towards universal driving perception across camera configurations.arXiv preprint arXiv:2410.13864, 2024. 63, 65, 73, 74, 75, 163

2024 arXiv
[130]

Unsuperviseddomainadaptationformonocular 3Dobjectdetectionviaself-training

Zhenyu Li, Zehui Chen, Ang Li, Liangji Fang, Qinhong Jiang, Xianming Liu, and Junjun Jiang. Unsuperviseddomainadaptationformonocular 3Dobjectdetectionviaself-training. InECCV, 2022. 65

2022
[131]

BEVNeXt: Reviving dense BEV frameworks for3D object detection

Zhenxin Li, Shiyi Lan, Jose Alvarez, and Zuxuan Wu. BEVNeXt: Reviving dense BEV frameworks for3D object detection. InCVPR, 2024. 65

2024
[132]

InECCV, 2022

Zhiqi Li, Wenhai Wang, Hongyang Li, Enze Xie, Chonghao Sima, Tong Lu, Yu Qiao, and JifengDai.BEVFormer: Learningbird’s-eye-viewrepresentationfrommulti-cameraimages via spatiotemporal transformers. InECCV, 2022. 1, 44, 46, 48, 53, 59, 61, 147, 155

2022
[133]

Unimode: Unifiedmonoc- ular 3D object detection

ZhuolingLi,XiaogangXu,SerNamLim,andHengshuangZhao. Unimode: Unifiedmonoc- ular 3D object detection. InCVPR, 2024. 61

2024
[134]

FB- BEV: BEV representation from forward-backward view transformations

Zhiqi Li, Zhiding Yu, Wenhai Wang, Anima Anandkumar, Tong Lu, and Jose Alvarez. FB- BEV: BEV representation from forward-backward view transformations. InICCV, 2023. 59

2023
[135]

Geometry-aware data augmentation for monocular3D object detection.arXiv preprint arXiv:2104.05858, 2021

Qing Lian, Botao Ye, Ruĳia Xu, Weilong Yao, and Tong Zhang. Geometry-aware data augmentation for monocular3D object detection.arXiv preprint arXiv:2104.05858, 2021. 26, 29

2021 arXiv
[136]

KITTI-360: A novel dataset and benchmarks for urban scene understanding in2D and3D

Yiyi Liao, Jun Xie, and Andreas Geiger. KITTI-360: A novel dataset and benchmarks for urban scene understanding in2D and3D. TPAMI, 2022. 45, 46, 53, 54, 60, 148, 149

2022
[137]

MonoTTA: Fully test-time adaptation for monocular3D object detection

Hongbin Lin, Yifan Zhang, Shuaicheng Niu, Shuguang Cui, and Zhen Li. MonoTTA: Fully test-time adaptation for monocular3D object detection. InECCV, 2024. 61

2024
[138]

Feature pyramid networks for object detection

Tsung-Yi Lin, Piotr Dollár, Ross Girshick, Kaiming He, Bharath Hariharan, and Serge Belongie. Feature pyramid networks for object detection. InCVPR, 2017. 39, 123, 149

2017
[139]

Focal loss for dense object detection.TPAMI, 2018

Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollár. Focal loss for dense object detection.TPAMI, 2018. 8, 9

2018
[140]

RayDenoising: Depth-awarehardnegativesamplingformulti-view 3D object detection

Feng Liu, Tengteng Huang, Qianjing Zhang, Haotian Yao, Chi Zhang, Fang Wan, Qixiang Ye,andYanzhaoZhou. RayDenoising: Depth-awarehardnegativesamplingformulti-view 3D object detection. InECCV, 2024. 65 89

2024
[141]

InNeurIPS, 2021

FengLiuandXiaomingLiu.Voxel-based 3Ddetectionandreconstructionofmultipleobjects from a single image. InNeurIPS, 2021. 48, 65

2021
[142]

SparseBEV: High-performance sparse 3D object detection from multi-camera videos

Haisong Liu Liu, Yao Teng Teng, Tao Lu, Haiguang Wang, and Limin Wang. SparseBEV: High-performance sparse 3D object detection from multi-camera videos. InICCV, 2023. 59, 154

2023
[143]

Deep fitting degree scoring network for monocular3D object detection

Lĳie Liu, Jiwen Lu, Chunjing Xu, Qi Tian, and Jie Zhou. Deep fitting degree scoring network for monocular3D object detection. InCVPR, 2019. 9, 19, 25, 29

2019
[144]

Reinforced axial refinement network for monocular3D object detection

Lĳie Liu, Chufan Wu, Jiwen Lu, Lingxi Xie, Jie Zhou, and Qi Tian. Reinforced axial refinement network for monocular3D object detection. InECCV, 2020. 19

2020
[145]

Adaptive NMS: Refining pedestrian detection in a crowd

Songtao Liu, Di Huang, and Yunhong Wang. Adaptive NMS: Refining pedestrian detection in a crowd. InCVPR, 2019. 9, 16, 17

2019
[146]

Learning auxiliary monocular contexts helps monocular 3D object detection

Xianpeng Liu, Nan Xue, and Tianfu Wu. Learning auxiliary monocular contexts helps monocular 3D object detection. InAAAI, 2022. 29

2022
[147]

Monocular 3Dobjectdetectionwithboundingboxdenoisingin 3Dbyperceiver

XianpengLiu, CeZheng, KelvinCheng, NanXue, Guo-JunQi, andTianfuWu. Monocular 3Dobjectdetectionwithboundingboxdenoisingin 3Dbyperceiver. In ICCV,2023. 48,65

2023
[148]

Multi-view attentive contextualization for multi-view3D object detection

Xianpeng Liu, Ce Zheng, Ming Qian, Nan Xue, Chen Chen, Zhebin Zhang, Chen Li, and Tianfu Wu. Multi-view attentive contextualization for multi-view3D object detection. In CVPR, 2024. 65

2024
[149]

PETR: Position embedding transformation for multi-view3D object detection

Yingfei Liu, Tiancai Wang, Xiangyu Zhang, and Jian Sun. PETR: Position embedding transformation for multi-view3D object detection. InECCV, 2022. 155

2022
[150]

PETRv2: A unified framework for3D perception from multi-camera images

Yingfei Liu, Junjie Yan, Fan Jia, Shuailin Li, Qi Gao, Tiancai Wang, Xiangyu Zhang, and Jian Sun. PETRv2: A unified framework for3D perception from multi-camera images. In ICCV, 2023. 48, 59, 65, 155

2023
[151]

Ground-awaremonocular 3Dobjectdetectionfor autonomous driving.Robotics and Automation Letters, 2021

YuxuanLiu,YuanYixuan,andMingLiu. Ground-awaremonocular 3Dobjectdetectionfor autonomous driving.Robotics and Automation Letters, 2021. 26, 35, 36

2021
[152]

Swin transformer: Hierarchical vision transformer using shifted windows

ZeLiu,YutongLin,YueCao,HanHu,YixuanWei,ZhengZhang,StephenLin,andBaining Guo. Swin transformer: Hierarchical vision transformer using shifted windows. InICCV,
[153]

SMOKE: Single-stage monocular3D object detection via keypoint estimation

Zechen Liu, Zizhang Wu, and Roland Tóth. SMOKE: Single-stage monocular3D object detection via keypoint estimation. InCVPR Workshops, 2020. 19

2020
[154]

AutoShape: Real- time shape-aware monocular3D object detection

Zongdai Liu, Dingfu Zhou, Feixiang Lu, Jin Fang, and Liangjun Zhang. AutoShape: Real- time shape-aware monocular3D object detection. InICCV, 2021. 29, 35, 48, 65

2021
[155]

RADIANT: RADar Image Association Network for3D object detection

Yunfei Long, Abhinav Kumar, Daniel Morris, Xiaoming Liu, Marcos Castro, and Punarjay Chakravarty. RADIANT: RADar Image Association Network for3D object detection. In 90 AAAI, 2023. 1, 47, 64

2023
[156]

Decoupled weight decay regularization

Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. InICLR, 2019. 150, 151

2019
[157]

Distinctive image features from scale-invariant keypoints.ĲCV, 2004

David Lowe. Distinctive image features from scale-invariant keypoints.ĲCV, 2004. 9

2004
[158]

Towards gen- eralizable multi-camera 3D object detection via perspective debiasing

Hao Lu, Yunpeng Zhang, Qing Lian, Dalong Du, and Yingcong Chen. Towards gen- eralizable multi-camera 3D object detection via perspective debiasing. arXiv preprint arXiv:2310.11346, 2023. 65

2023 arXiv
[159]

Geometry uncertainty projection network for monocular3D object detection

Yan Lu, Xinzhu Ma, Lei Yang, Tianzhu Zhang, Yating Liu, Qi Chu, Junjie Yan, and Wanli Ouyang. Geometry uncertainty projection network for monocular3D object detection. In ICCV, 2021. 25, 26, 29, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 48, 50, 55, 56, 57, 62, 65, 71, 73, 74, 75...

2021
[160]

M3DSSD: Monocular3D single stage object detector

Shujie Luo, Hang Dai, Ling Shao, and Yong Ding. M3DSSD: Monocular3D single stage object detector. InCVPR, 2021. 19

2021
[161]

DETR4D: Direct multi- view 3D object detection with sparse attention.arXiv preprint arXiv:2212.07849, 2022

Zhipeng Luo, Changqing Zhou, Gongjie Zhang, and Shĳian Lu. DETR4D: Direct multi- view 3D object detection with sparse attention.arXiv preprint arXiv:2212.07849, 2022. 48

2022 arXiv
[162]

Rethinking Pseudo-LiDAR representation

Xinzhu Ma, Shinan Liu, Zhiyi Xia, Hongwen Zhang, Xingyu Zeng, and Wanli Ouyang. Rethinking Pseudo-LiDAR representation. InECCV, 2020. 29, 42

2020
[163]

29, 48, 65

Xinzhu Ma, Wanli Ouyang, Andrea Simonelli, and Elisa Ricci.3D object detection from images for autonomous driving: A survey.TPAMI, 2023. 29, 48, 65

2023
[164]

Towards fair and comprehensive comparisons for image-based3D object detection

Xinzhu Ma, Yongtao Wang, Yinmin Zhang, Zhiyi Xia, Yuan Meng, Zhihui Wang, Haojie Li, and Wanli Ouyang. Towards fair and comprehensive comparisons for image-based3D object detection. InICCV, 2023. 48

2023
[165]

Accu- rate monocular3D object detection via color-embedded3D reconstruction for autonomous driving

Xinzhu Ma, Zhihui Wang, Haojie Li, Pengbo Zhang, Wanli Ouyang, and Xin Fan. Accu- rate monocular3D object detection via color-embedded3D reconstruction for autonomous driving. InICCV, 2019. 19, 29, 48, 65

2019
[166]

Delving into localization errors for monocular3D object detection

Xinzhu Ma, Yinmin Zhang, Dan Xu, Dongzhan Zhou, Shuai Yi, Haojie Li, and Wanli Ouyang. Delving into localization errors for monocular3D object detection. In CVPR,
[167]

Vision-centricBEVperception: Asurvey

Yuexin Ma, Tai Wang, Xuyang Bai, Huitong Yang, Yuenan Hou, Yaming Wang, Yu Qiao, RuigangYang,DineshManocha,andXingeZhu. Vision-centricBEVperception: Asurvey. arXiv preprint arXiv:2208.02797, 2022. 46, 48, 58, 65

2022 arXiv
[168]

Enabling equivariance for arbitrary lie groups

Lachlan MacDonald, Sameera Ramasinghe, and Simon Lucey. Enabling equivariance for arbitrary lie groups. InCVPR, 2022. 63 91

2022
[169]

Roi-10D: Monocular lifting of2D detection to6D pose and metric shape

Fabian Manhardt, Wadim Kehl, and Adrien Gaidon. Roi-10D: Monocular lifting of2D detection to6D pose and metric shape. InCVPR, 2019. 19

2019
[170]

Scale equivariance in CNNs with vector fields

Diego Marcos, Benjamin Kellenberger, Sylvain Lobry, and Devis Tuia. Scale equivariance in CNNs with vector fields. InICML Workshops, 2018. 28

2018
[171]

Rotation equivariant vector field networks

Diego Marcos, Michele Volpi, Nikos Komodakis, and Devis Tuia. Rotation equivariant vector field networks. InICCV, 2017. 28

2017
[172]

Symmetry and uncertainty-aware object SLAM for6DoF object pose estimation

NathanielMerrill,YuliangGuo,XingxingZuo,XinyuHuang,StefanLeutenegger,XiPeng, Liu Ren, and Guoquan Huang. Symmetry and uncertainty-aware object SLAM for6DoF object pose estimation. InCVPR, 2022. 1, 44, 61

2022
[173]

Neural network for graphs: A contextual constructive approach.IEEE Transactions on Neural Networks, 2009

Alessio Micheli. Neural network for graphs: A contextual constructive approach.IEEE Transactions on Neural Networks, 2009. 28

2009
[174]

Scale&affineinvariantinterestpointdetectors

KrystianMikolajczykandCordeliaSchmid. Scale&affineinvariantinterestpointdetectors. ĲCV, 2004. 9

2004
[175]

NeurOCS: Neural NOCS supervision for monocular3D object localization

ZhixiangMin,BingbingZhuang,SamuelSchulter,BuyuLiu,EnriqueDunn,andManmohan Chandraker. NeurOCS: Neural NOCS supervision for monocular3D object localization. In CVPR, 2023. 48, 65

2023
[176]

Lie group decompositions for equivariant neural networks

Mircea Mironenco and Patrick Forré. Lie group decompositions for equivariant neural networks. InICLR, 2024. 63

2024
[177]

Rotation matters: Generalized monocular 3D object detection for various camera systems.arXiv preprint arXiv:2310.05366, 2023

SungHo Moon, JinWoo Bae, and SungHoon Im. Rotation matters: Generalized monocular 3D object detection for various camera systems.arXiv preprint arXiv:2310.05366, 2023. 1, 44, 61, 65

2023 arXiv
[178]

Segmentation of3D LiDAR data in non-flat urban environments using a local convexity criterion

Frank Moosmann, Oliver Pink, and Christoph Stiller. Segmentation of3D LiDAR data in non-flat urban environments using a local convexity criterion. InIntelligent Vehicles Symposium, 2009. 9

2009
[179]

InECCV, 2024

YoungminOh,Hyung-IlKim,SeongTaeKim,andJungKim.MonoWAD:Weather-adaptive diffusion model for robust monocular3D object detection. InECCV, 2024. 61

2024
[180]

Cross-view semantic segmentation for sensing surroundings.RAL, 2020

Bowen Pan, Jiankai Sun, Ho Leung, Alex Andonian, and Bolei Zhou. Cross-view semantic segmentation for sensing surroundings.RAL, 2020. 48

2020
[181]

Is Pseudo-LiDAR needed for monocular3D object detection? InICCV, 2021

Dennis Park, Rares Ambrus, Vitor Guizilini, Jie Li, and Adrien Gaidon. Is Pseudo-LiDAR needed for monocular3D object detection? InICCV, 2021. 1, 29, 35, 36, 37, 44, 61, 127, 132, 150

2021
[182]

Time will tell: New outlooks and a baseline for temporal multi-view3D object detection

Jinhyung Park, Chenfeng Xu, Shĳia Yang, Kurt Keutzer, Kris Kitani, Masayoshi Tomizuka, and Wei Zhan. Time will tell: New outlooks and a baseline for temporal multi-view3D object detection. InICLR, 2023. 48, 65, 155 92

2023
[183]

Pix2Pose: Pixel-wisecoordinateregression of objects for6D pose estimation

KiruPark,TimothyPatten,andMarkusVincze. Pix2Pose: Pixel-wisecoordinateregression of objects for6D pose estimation. InICCV, 2019. 1, 44, 61

2019
[184]

PyTorch: An imperative style, high-performance deep learning library

AdamPaszke,SamGross,FranciscoMassa,AdamLerer,JamesBradbury,GregoryChanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf,EdwardYang,ZacharyDeVito,MartinRaison,AlykhanTejani,SasankChilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and...

2019
[185]

Gradient estimation with stochastic softmax tricks

Max Paulus, Dami Choi, Daniel Tarlow, Andreas Krause, and Chris Maddison. Gradient estimation with stochastic softmax tricks. InNeurIPS, 2020. 11, 15

2020
[186]

Fromcontoursto 3Dobjectdetectionandposeestimation

NadiaPayetandSinisaTodorovic. Fromcontoursto 3Dobjectdetectionandposeestimation. InICCV, 2011. 9, 29, 47, 64

2011
[187]

Multi-viewand 3Ddeformable part models.TPAMI, 2015

BojanPepik,MichaelStark,PeterGehler,andBerntSchiele. Multi-viewand 3Ddeformable part models.TPAMI, 2015. 9, 29

2015
[188]

Lift, splat, shoot: Encoding images from arbitrary camera rigs by implicitly unprojecting to3D

Jonah Philion and Sanja Fidler. Lift, splat, shoot: Encoding images from arbitrary camera rigs by implicitly unprojecting to3D. InECCV, 2020. 48

2020
[189]

GD Baedeker, 1828

Julius Plücker.Analytisch-geometrische Entwicklungen. GD Baedeker, 1828. 73, 74
[190]

Differen- tiation of blackbox combinatorial solvers

Marin Pogančić, Anselm Paulus, Vit Musil, Georg Martius, and Michal Rolinek. Differen- tiation of blackbox combinatorial solvers. InICLR, 2019. 11

2019
[191]

Softsort: A continuous relaxation for the argsort operator

Sebastian Prillo and Julian Eisenschlos. Softsort: A continuous relaxation for the argsort operator. InICML, 2020. 11, 12

2020
[192]

Learning to filter object detections

Sergey Prokudin, Daniel Kappler, Sebastian Nowozin, and Peter Gehler. Learning to filter object detections. InGCPR, 2017. 6, 7, 8, 9, 11, 12, 16, 17, 18, 24, 105

2017
[193]

Don’t blame dataset shift! shortcut learning due to gradients and cross entropy

Aahlad Manas Puli, Lily Zhang, Yoav Wald, and Rajesh Ranganath. Don’t blame dataset shift! shortcut learning due to gradients and cross entropy. InNeurIPS, 2023. 64

2023
[194]

Deephoughvotingfor 3Dobject detection in point clouds

CharlesQi,OrLitany,KaimingHe,andLeonidasGuibas. Deephoughvotingfor 3Dobject detection in point clouds. InICCV, 2019. 56

2019
[195]

MonoGRNet: A geometric reasoning network for 3D object localization

Zengyi Qin, Jinglu Wang, and Yan Lu. MonoGRNet: A geometric reasoning network for 3D object localization. InAAAI, 2019. 19, 20

2019
[196]

MonoDiff: Monocular3D object detection and pose estimation with diffusion models

Yasiru Ranasinghe, Deepti Hegde, and Vishal M Patel. MonoDiff: Monocular3D object detection and pose estimation with diffusion models. InCVPR, 2024. 65

2024
[197]

SemVecNet: Generalizable vector map generation for arbitrary sensor configurations

NarayananElavathurRanganatha,HengyuanZhang,ShashankVenkatramani,Jing-YanLiao, and Henrik Christensen. SemVecNet: Generalizable vector map generation for arbitrary sensor configurations. 2024. 65 93

2024
[198]

Boostingdeepneuralnetworkswithgeometrical prior knowledge: A survey.arXiv preprint arXiv:2006.16867, 2020

MatthiasRathandAlexandruCondurache. Boostingdeepneuralnetworkswithgeometrical prior knowledge: A survey.arXiv preprint arXiv:2006.16867, 2020. 25, 28, 29, 114

2006 arXiv
[199]

Categorical depth distribu- tionnetworkformonocular 3Dobjectdetection

Cody Reading, Ali Harakeh, Julia Chae, and Steven Waslander. Categorical depth distribu- tionnetworkformonocular 3Dobjectdetection. In CVPR,2021. 29,35,36,42,48,65,124, 126, 132

2021
[200]

You only look once: Unified, real-time object detection

Joseph Redmon, Santosh Divvala, Ross Girshick, and Ali Farhadi. You only look once: Unified, real-time object detection. InCVPR, 2016. 8, 9

2016
[201]

Soccer on your tabletop

KonstantinosRematas,IraKemelmacher-Shlizerman,BrianCurless,andSteveSeitz. Soccer on your tabletop. InCVPR, 2018. 6, 25

2018
[202]

FasterR-CNN:Towardsreal-time object detection with region proposal networks

ShaoqingRen,KaimingHe,RossGirshick,andJianSun. FasterR-CNN:Towardsreal-time object detection with region proposal networks. InNeurIPS, 2015. 6, 8, 9, 25

2015
[203]

Automaticdetectionand segmentationofevolvingprocessesin 3Dmedicalimages: Applicationtomultiplesclerosis

DavidRey,GérardSubsol,HervéDelingette,andNicholasAyache. Automaticdetectionand segmentationofevolvingprocessesin 3Dmedicalimages: Applicationtomultiplesclerosis. Medical Image Analysis, 2002. 6

2002
[204]

Generalized intersection over union: A metric and a loss for bounding box regression

Hamid Rezatofighi, Nathan Tsoi, JunYoung Gwak, Amir Sadeghian, Ian Reid, and Silvio Savarese. Generalized intersection over union: A metric and a loss for bounding box regression. InCVPR, 2019. 16, 17

2019
[205]

Predictingsemanticmaprepresentationsfromimages using pyramid occupancy networks

ThomasRoddickandRobertoCipolla. Predictingsemanticmaprepresentationsfromimages using pyramid occupancy networks. InCVPR, 2020. 48

2020
[206]

Edge and curve detection for visual scene analysis

Azriel Rosenfeld and Mark Thurston. Edge and curve detection for visual scene analysis. IEEE Transactions on Computers, 1971. 9

1971
[207]

Learning qualitatively diverse and interpretable rules for classification

Andrew Slavin Ross, Weiwei Pan, and Finale Doshi-Velez. Learning qualitatively diverse and interpretable rules for classification. InICML Workshops, 2018. 64

2018
[208]

Towards viewpoint-invariant visual recognition via adversarial training

Shouwei Ruan, Yinpeng Dong, Hang Su, Jianteng Peng, Ning Chen, and Xingxing Wei. Towards viewpoint-invariant visual recognition via adversarial training. InICCV, 2023. 64

2023
[209]

Optimizedpedestriandetectionformultipleand occluded people

SitapaRujikietgumjornandRobertCollins. Optimizedpedestriandetectionformultipleand occluded people. InCVPR, 2013. 9, 15, 17

2013
[210]

Distributionally robust neural networks for group shifts: On the importance of regularization for worst-case generalization

Shiori Sagawa, Pang Wei Koh, Tatsunori B Hashimoto, and Percy Liang. Distributionally robust neural networks for group shifts: On the importance of regularization for worst-case generalization. InICLR, 2019. 64

2019
[211]

Translatingimagesinto maps

AvishkarSaha,OscarMendez,ChrisRussell,andRichardBowden. Translatingimagesinto maps. InICRA, 2022. 48, 50, 54, 55, 56, 57, 149, 150, 152

2022
[212]

Shadows don’t lie and lines can’t bend! generative models don’t know 94 projective geometry

Ayush Sarkar, Hanlin Mai, Amitabh Mahapatra, Svetlana Lazebnik, David Forsyth, and Anand Bhattad. Shadows don’t lie and lines can’t bend! generative models don’t know 94 projective geometry... for now. InCVPR, 2024. 63

2024
[213]

Robotic grasping of novel objects using vision.ĲRR, 2008

Ashutosh Saxena, Justin Driemeyer, and Andrew Ng. Robotic grasping of novel objects using vision.ĲRR, 2008. 1, 6, 25, 44, 61

2008
[214]

Pegasos: Primal estimated sub- gradient solver for SVM

Shai Shalev-Shwartz, Yoram Singer, and Nathan Srebro. Pegasos: Primal estimated sub- gradient solver for SVM. InICML, 2007. 49, 50, 51, 138, 141

2007
[215]

PointRCNN: 3D object proposal generation and detection from point cloud

Shaoshuai Shi, Xiaogang Wang, and Hongsheng Li. PointRCNN: 3D object proposal generation and detection from point cloud. InCVPR, 2019. 1, 9, 29, 47, 48, 64

2019
[216]

Distance-normalized unified representa- tion for monocular3D object detection

Xuepeng Shi, Zhixiang Chen, and Tae-Kyun Kim. Distance-normalized unified representa- tion for monocular3D object detection. InECCV, 2020. 6, 7, 9, 15, 17, 19, 21, 23, 24, 48, 108, 109

2020
[217]

Multivariate probabilistic monocular3D object detection

Xuepeng Shi, Zhixiang Chen, and Tae-Kyun Kim. Multivariate probabilistic monocular3D object detection. InWACV, 2023. 47

2023
[218]

Geometry-baseddistancedecompositionformonocular 3Dobjectdetection

XuepengShi, QiYe, XiaozhiChen, ChuangrongChen, ZhixiangChen, andTae-KyunKim. Geometry-baseddistancedecompositionformonocular 3Dobjectdetection. In ICCV,2021. 26, 35, 36, 37, 128, 129, 132

2021
[219]

3DPPE: 3D point positional encoding for multi-camera 3D object detection transformers

Changyong Shu, Fisher Yu, and Yifan Liu. 3DPPE: 3D point positional encoding for multi-camera 3D object detection transformers. InICCV, 2023. 48, 59, 65, 155

2023
[220]

Disentangling monocular 3D object detection: From single to multi-class recognition

AndreaSimonelli,SamuelBulò,LorenzoPorzi,ManuelAntequera,andPeterKontschieder. Disentangling monocular 3D object detection: From single to multi-class recognition. TPAMI, 2020. 6, 7, 9, 18, 19, 20, 25, 34, 35, 36, 128, 132

2020
[221]

Are we missing confidence in Pseudo-LiDAR methods for monocular3D object detection? In ICCV, 2021

Andrea Simonelli, Samuel Bulò, Lorenzo Porzi, Peter Kontschieder, and Elisa Ricci. Are we missing confidence in Pseudo-LiDAR methods for monocular3D object detection? In ICCV, 2021. 29, 35, 36, 109, 130

2021
[222]

Disentangling monocular3D object detection

Andrea Simonelli, Samuel Bulò, Lorenzo Porzi, Manuel López-Antequera, and Peter Kontschieder. Disentangling monocular3D object detection. InICCV, 2019. 7, 19, 20, 34, 128

2019
[223]

InECCV, 2020

AndreaSimonelli,SamuelBulò,LorenzoPorzi,ElisaRicci,andPeterKontschieder.Towards generalization across depth for monocular3D object detection. InECCV, 2020. 9, 19, 20, 25, 26, 29

2020
[224]

Determinantalpointprocessasan alternative to NMS

SamikSome,MithunDasGupta,andVinayNamboodiri. Determinantalpointprocessasan alternative to NMS. InBMVC, 2020. 9, 15, 17

2020
[225]

DISCO: accurate discrete scale convolutions

Ivan Sosnovik, Artem Moskalev, and Arnold Smeulders. DISCO: accurate discrete scale convolutions. InBMVC, 2021. 39, 41

2021
[226]

Scale equivariance improves 95 siamese tracking

Ivan Sosnovik, Artem Moskalev, and Arnold Smeulders. Scale equivariance improves 95 siamese tracking. InWACV, 2021. 28, 32, 34, 40, 122, 123, 124

2021
[227]

InICLR, 2020

IvanSosnovik,MichałSzmaja,andArnoldSmeulders.Scale-equivariantsteerablenetworks. InICLR, 2020. 27, 28, 31, 32, 33, 38, 39, 41, 119, 120, 122, 123, 132

2020
[228]

Wide-baseline stereo from multiple views: a probabilistic account

Christoph Strecha, Rik Fransens, and Luc Van Gool. Wide-baseline stereo from multiple views: a probabilistic account. InCVPR, 2004. 66

2004
[229]

Dense matching of multiple wide- baseline views

Christoph Strecha, Tinne Tuytelaars, and Luc Van Gool. Dense matching of multiple wide- baseline views. InICCV, 2003. 66

2003
[230]

InCVPR, 2020

Pei Sun, Henrik Kretzschmar, Xerxes Dotiwalla, Aurelien Chouard, Vĳaysai Patnaik, Paul Tsui, James Guo, Yin Zhou, Yuning Chai, Benjamin Caine, Vĳay Vasudevan, Wei Han, Jiquan Ngiam, Hang Zhao, Aleksei Timofeev, Scott Ettinger, Maxim Krivokon, Amy Gao, AdityaJoshi,YuZhang,Jonat...

2020
[231]

EfficientDet: Scalable and efficient object detection

Mingxing Tan, Ruoming Pang, and Quoc Le. EfficientDet: Scalable and efficient object detection. InCVPR, 2020. 150

2020
[232]

Center 3D:Center-basedmonocular 3Dobjectdetectionwithjointdepthunderstanding

YunleiTang,SebastianDorn,andChiragkumarSavani. Center 3D:Center-basedmonocular 3Dobjectdetectionwithjointdepthunderstanding. arXivpreprintarXiv:2005.13423 ,2020. 8, 9, 25, 26, 29

2005 arXiv
[233]

SimPB: A single model for 2D and3D object detection from multiple cameras

Yingqi Tang, Zhaotie Meng, Guoliang Chen, and Erkang Cheng. SimPB: A single model for 2D and3D object detection from multiple cameras. InECCV, 2024. 65

2024
[234]

Determining the lines through four lines.Journal of graphics tools, 1999

Seth Teller and Michael Hohmeyer. Determining the lines through four lines.Journal of graphics tools, 1999. 74

1999
[235]

Unshuffling data for improved gen- eralization in visual question answering

Damien Teney, Ehsan Abbasnejad, and Anton Hengel. Unshuffling data for improved gen- eralization in visual question answering. InICCV, 2021. 64

2021
[236]

Evadingthesimplicity bias: TrainingadiversesetofmodelsdiscoverssolutionswithsuperiorOODgeneralization

DamienTeney,EhsanAbbasnejad,SimonLucey,andAntonHengel. Evadingthesimplicity bias: TrainingadiversesetofmodelsdiscoverssolutionswithsuperiorOODgeneralization. InCVPR, 2022. 64

2022
[237]

IDandOODperformance are sometimes inversely correlated on real-world datasets

DamienTeney,YongLin,SeongJoonOh,andEhsanAbbasnejad. IDandOODperformance are sometimes inversely correlated on real-world datasets. InNeurIPS, 2023. 63, 64

2023
[238]

Exploring 2Ddataaugmentationfor 3Dmonocularobjectdetection

SugirthaThayalan-Vaz,SrideviM,KhailashSanthakumar,BRaviKiran,ThomasGauthier, andSenthilYogamani. Exploring 2Ddataaugmentationfor 3Dmonocularobjectdetection. arXiv preprint arXiv:2104.10786, 2021. 26, 29

2021 arXiv
[239]

Tensor field networks: Rotation-and translation-equivariant neural networks for 3D point clouds.arXiv preprint arXiv:1802.08219, 2018

Nathaniel Thomas, Tess Smidt, Steven Kearnes, Lusann Yang, Li Li, Kai Kohlhoff, and Patrick Riley. Tensor field networks: Rotation-and translation-equivariant neural networks for 3D point clouds.arXiv preprint arXiv:1802.08219, 2018. 28 96

2018 arXiv
[240]

Overcoming simplicity bias in deep networks using a feature sieve

Rishabh Tiwari and Pradeep Shenoy. Overcoming simplicity bias in deep networks using a feature sieve. InICML, 2023. 64

2023
[241]

ImprovingobjectlocalizationwithfitnessNMS and bounded IoU loss

LachlanTychsen-SmithandLarsPetersson. ImprovingobjectlocalizationwithfitnessNMS and bounded IoU loss. InCVPR, 2018. 19, 24

2018
[242]

Pose-independent automatic target detection and recognition using3D laser radar imagery.Lincoln laboratory journal, 2005

Alexandru Vasile and Richard Marino. Pose-independent automatic target detection and recognition using3D laser radar imagery.Lincoln laboratory journal, 2005. 9

2005
[243]

Rapid object detection using a boosted cascade of simple features

Paul Viola and Michael Jones. Rapid object detection using a boosted cascade of simple features. InCVPR, 2001. 9

2001
[244]

End-to-end integration of a convolution network, deformable parts model and non-maximum suppression

Li Wan, David Eigen, and Rob Fergus. End-to-end integration of a convolution network, deformable parts model and non-maximum suppression. InCVPR, 2015. 9, 16, 17

2015
[245]

Depth-conditioned dynamic message propagation for monocular3D object detection

Li Wang, Liang Du, Xiaoqing Ye, Yanwei Fu, Guodong Guo, Xiangyang Xue, Jianfeng Feng, and Li Zhang. Depth-conditioned dynamic message propagation for monocular3D object detection. InCVPR, 2021. 36

2021
[246]

Progressive coordinatetransformsformonocular 3Dobjectdetection

Li Wang, Li Zhang, Yi Zhu, Zhi Zhang, Tong He, Mu Li, and Xiangyang Xue. Progressive coordinatetransformsformonocular 3Dobjectdetection. In NeurIPS,2021. 35,36,42,132

2021
[247]

Incorporatingsymmetryintodeepdynamicsmodels for improved generalization

RuiWang,RobinWalters,andRoseYu. Incorporatingsymmetryintodeepdynamicsmodels for improved generalization. InICLR, 2021. 28

2021
[248]

StreamPETR: Exploring object-centric temporal modeling for efficient multi-view3D object detection

Shihao Wang, Yingfei Liu, Tiancai Wang, Ying Li, and Xiangyu Zhang. StreamPETR: Exploring object-centric temporal modeling for efficient multi-view3D object detection. In ICCV, 2023. 48, 65

2023
[249]

Towardsdomaingeneralizationformulti-view 3Dobjectdetectionin bird-eye-view

Shuo Wang, Xinhai Zhao, Hai-Ming Xu, Zehui Chen, Dameng Yu, Jiahao Chang, Zhen Yang,andFengZhao. Towardsdomaingeneralizationformulti-view 3Dobjectdetectionin bird-eye-view. InCVPR, 2023. 65

2023
[250]

FCOS3D: Fully convolutional one-stage monocular3D object detection

Tai Wang, Xinge Zhu, Jiangmiao Pang, and Dahua Lin. FCOS3D: Fully convolutional one-stage monocular3D object detection. InICCV Workshops, 2021. 155

2021
[251]

Probabilistic and geometric depth: Detecting objects in perspective

Tai Wang, Xinge Zhu, Jiangmiao Pang, and Dahua Lin. Probabilistic and geometric depth: Detecting objects in perspective. InCoRL, 2021. 155

2021
[252]

Segmentation can aid detection: Segmentation-guided single stage detection for3D point cloud

Xueqing Wang, Diankun Zhang, Haoyu Niu, and Xiaojun Liu. Segmentation can aid detection: Segmentation-guided single stage detection for3D point cloud. Electronics,
[253]

Scale- equalizing pyramid convolution for object detection

Xinjiang Wang, Shilong Zhang, Zhuoran Yu, Litong Feng, and Wayne Zhang. Scale- equalizing pyramid convolution for object detection. InCVPR, 2020. 127

2020
[254]

Pseudo-LiDAR from visual depth estimation: Bridging the gap in3D object 97 detection for autonomous driving

Yan Wang, Wei-Lun Chao, Divyansh Garg, Bharath Hariharan, Mark Campbell, and Kilian Weinberger. Pseudo-LiDAR from visual depth estimation: Bridging the gap in3D object 97 detection for autonomous driving. InCVPR, 2019. 9, 29, 43, 48, 65

2019
[255]

Train in Germany, test in the USA: Making3D object detectors generalize

Yan Wang, Xiangyu Chen, Yurong You, Li Li, Bharath Hariharan, Mark Campbell, Kilian Weinberger, and Wei-Lun Chao. Train in Germany, test in the USA: Making3D object detectors generalize. InCVPR, 2020. 65, 129

2020
[256]

FrustumFormer: Adaptiveinstance-aware resampling for multi-view3D detection

YuqiWang,YuntaoChen,andZhaoxiangZhang. FrustumFormer: Adaptiveinstance-aware resampling for multi-view3D detection. InCVPR, 2023. 59

2023
[257]

DETR3D: 3D object detection from multi-view images via3D-to-2D queries

Yue Wang, Vitor Guizilini, Tianyuan Zhang, Yilun Wang, Hang Zhao, and Justin Solomon. DETR3D: 3D object detection from multi-view images via3D-to-2D queries. In CoRL,
[258]

Image quality assessment: from error visibility to structural similarity.TIP, 2004

Zhou Wang, Alan Bovik, Hamid Sheikh, and Eero Simoncelli. Image quality assessment: from error visibility to structural similarity.TIP, 2004. 39

2004
[259]

Object as Query: Lifting any 2D object detector to3D detection

Zitian Wang, Zehao Huang, Jiahui Fu, Naiyan Wang, and Si Liu. Object as Query: Lifting any 2D object detector to3D detection. InICCV, 2023. 59

2023
[260]

DistillBEV: Boosting multi-camera 3D object detection with cross-modal knowledge distillation

Zeyu Wang, Dingwen Li, Chenxu Luo, Cihang Xie, and Xiaodong Yang. DistillBEV: Boosting multi-camera 3D object detection with cross-modal knowledge distillation. In ICCV, 2023. 48, 65

2023
[261]

STS: Surround-view temporal stereo for multi-view3D detection

Zengran Wang, Chen Min, Zheng Ge, Yinhao Li, Zeming Li, Hongyu Yang, and Di Huang. STS: Surround-view temporal stereo for multi-view3D detection. InAAAI, 2023. 48, 65, 155

2023
[262]

Coordinate independent convolutional networks–isometry and gauge equivariant convolutions on riemannian mani- folds

Maurice Weiler, Patrick Forré, Erik Verlinde, and Max Welling. Coordinate independent convolutional networks–isometry and gauge equivariant convolutions on riemannian mani- folds. arXiv preprint arXiv:2106.06020, 2021. 28

2021 arXiv
[263]

Learning steerable filters for rotation equivariant CNNs

Maurice Weiler, Fred Hamprecht, and Martin Storath. Learning steerable filters for rotation equivariant CNNs. InCVPR, 2018. 28

2018
[264]

Learning invariances using the marginal likelihood

Mark van der Wilk, Matthias Bauer, ST John, and James Hensman. Learning invariances using the marginal likelihood. InNeurIPS, 2018. 28

2018
[265]

Cubenet: Equivariance to3D rotation and translation

Daniel Worrall and Gabriel Brostow. Cubenet: Equivariance to3D rotation and translation. InECCV, 2018. 28, 29, 38

2018
[266]

Harmonic networks: Deep translation and rotation equivariance

DanielWorrall,StephanGarbin,DaniyarTurmukhambetov,andGabrielBrostow. Harmonic networks: Deep translation and rotation equivariance. InCVPR, 2017. 28

2017
[267]

Deep scale-spaces: Equivariance over scale

Daniel Worrall and Max Welling. Deep scale-spaces: Equivariance over scale. InNeurIPS,
[268]

Waymo keynote talk, CVPR workshop on autonomous driving at 17:20

Chen Wu. Waymo keynote talk, CVPR workshop on autonomous driving at 17:20. https: //www.youtube.com/watch?v=fXsbI2VkHgc, 2023. Accessed: 2023-11-11. 2, 45 98

2023
[269]

MotionNet: Joint perception and motionpredictionforautonomousdrivingbasedonbird’seyeviewmaps

Pengxiang Wu, Siheng Chen, and Dimitris Metaxas. MotionNet: Joint perception and motionpredictionforautonomousdrivingbasedonbird’seyeviewmaps. In CVPR,2020. 9

2020
[270]

Rethinking “batch” in batchnorm

Yuxin Wu and Justin Johnson. Rethinking “batch” in batchnorm. arXiv preprint arXiv:2105.07576, 2021. 127

2021 arXiv
[271]

Subcategory-aware convolu- tional neural networks for object proposals and detection

Yu Xiang, Wongun Choi, Yuanqing Lin, and Silvio Savarese. Subcategory-aware convolu- tional neural networks for object proposals and detection. InWACV, 2017. 19

2017
[272]

Mˆ2BEV: Multi-camera joint3D detection and segmentation with unified birds-eye view representation.arXiv preprint arXiv:2204.05088, 2022

EnzeXie,ZhidingYu,DaquanZhou,JonahPhilion,AnimaAnandkumar,SanjaFidler,Ping Luo, and Jose Alvarez. Mˆ2BEV: Multi-camera joint3D detection and segmentation with unified birds-eye view representation.arXiv preprint arXiv:2204.05088, 2022. 48, 58

2022 arXiv
[273]

CAPE:Cameraviewpositionembeddingformulti-view 3Dobjectdetection

Kaixin Xiong, Shi Gong, Xiaoqing Ye, Xiao Tan, Ji Wan, Errui Ding, Jingdong Wang, and XiangBai. CAPE:Cameraviewpositionembeddingformulti-view 3Dobjectdetection. In CVPR, 2023. 59, 155

2023
[274]

3Difftection:3D object detection with geometry-aware diffusion features

Chenfeng Xu, Huan Ling, Sanja Fidler, and Or Litany. 3Difftection:3D object detection with geometry-aware diffusion features. InCVPR, 2024. 65

2024
[275]

MonoNeRD: NeRF-like representations for monocular3D object detection

JunkaiXu,LiangPeng,HaoranCheng,HaoLi,WeiQian,KeLi,WenxiaoWang,andDeng Cai. MonoNeRD: NeRF-like representations for monocular3D object detection. InICCV,
[276]

How neural networks extrapolate: From feedforward to graph neural networks

Keyulu Xu, Mozhi Zhang, Jingling Li, Simon Du, Ken-ichi Kawarabayashi, and Stefanie Jegelka. How neural networks extrapolate: From feedforward to graph neural networks. In ICLR, 2021. 63, 64

2021
[277]

SPG: Unsu- perviseddomainadaptationfor 3Dobjectdetectionviasemanticpointgeneration

Qiangeng Xu, Yin Zhou, Weiyue Wang, Charles Qi, and Dragomir Anguelov. SPG: Unsu- perviseddomainadaptationfor 3Dobjectdetectionviasemanticpointgeneration. In ICCV,
[278]

Scale-invariant convolutional neural networks.arXiv preprint arXiv:1411.6369, 2014

YichongXu,TianjunXiao,JiaxingZhang,KuiyuanYang,andZhengZhang. Scale-invariant convolutional neural networks.arXiv preprint arXiv:1411.6369, 2014. 28

2014 arXiv
[279]

MonoCD:Monoc- ular 3D object detection with complementary depths

LongfeiYan,PeiYan,ShengzhouXiong,XuanyuXiang,andYihuaTan. MonoCD:Monoc- ular 3D object detection with complementary depths. InCVPR, 2024. 65

2024
[280]

Upgradingopticalflowto 3Dsceneflowthroughoptical expansion

GengshanYangandDevaRamanan. Upgradingopticalflowto 3Dsceneflowthroughoptical expansion. InCVPR, 2020. 34

2020
[281]

LiDAR-based 3Dobjectdetectionviahybrid 2Dsemanticscenegeneration

HaitaoYang,ZaiweiZhang,XiangruHuang,MinBai,ChenSong,BoSun,LiErranLi,and QixingHuang. LiDAR-based 3Dobjectdetectionviahybrid 2Dsemanticscenegeneration. arXiv preprint arXiv:2304.01519, 2023. 48, 58, 59

2023 arXiv
[282]

ST3D:Self-training for unsupervised domain adaptation on3D object detection

JihanYang,ShaoshuaiShi,ZheWang,HongshengLi,andXiaojuanQi. ST3D:Self-training for unsupervised domain adaptation on3D object detection. InCVPR, 2021. 65 99

2021
[283]

Parametric depth based feature representation learning for object detection and segmentation in bird’s-eye view

Jiayu Yang, Enze Xie, Miaomiao Liu, and Jose Alvarez. Parametric depth based feature representation learning for object detection and segmentation in bird’s-eye view. InICCV,
[284]

GEDepth: Ground embedding for monocular depth estimation

Xiaodong Yang, Zhuang Ma, Zhiyu Ji, and Zhe Ren. GEDepth: Ground embedding for monocular depth estimation. InICCV, 2023. 67, 159, 160

2023
[285]

Im- proving out-of-distribution generalization of trajectory prediction for autonomous driving via polynomial representations.arXiv preprint arXiv:2407.13431, 2024

Yue Yao, Shengchao Yan, Daniel Goehring, Wolfram Burgard, and Joerg Reichardt. Im- proving out-of-distribution generalization of trajectory prediction for autonomous driving via polynomial representations.arXiv preprint arXiv:2407.13431, 2024. 65

2024 arXiv
[286]

Feature space particle inference for neural network ensembles

Shingo Yashima, Teppei Suzuki, Kohta Ishikawa, Ikuro Sato, and Rei Kawakami. Feature space particle inference for neural network ensembles. InICML, 2022. 64

2022
[287]

Monocular3D object detection via feature domain adaptation

Xiaoqing Ye, Liang Du, Yifeng Shi, Yingying Li, Xiao Tan, Jianfeng Feng, Errui Ding, and Shilei Wen. Monocular3D object detection via feature domain adaptation. InECCV, 2020. 19

2020
[288]

Chirality nets for human pose regression

Raymond Yeh, Yuan-Ting Hu, and Alexander Schwing. Chirality nets for human pose regression. InNeurIPS, 2019. 28

2019
[289]

Oriented object detection in aerial images with box boundary-aware vectors

JingruYi,PengxiangWu,BoLiu,QiaoyingHuang,HuiQu,andDimitrisMetaxas. Oriented object detection in aerial images with box boundary-aware vectors. InWACV, 2021. 55

2021
[290]

Center-based3D object detection and tracking

Tianwei Yin, Xingyi Zhou, and Philipp Krähenbühl. Center-based3D object detection and tracking. InCVPR, 2021. 1, 47, 64

2021
[291]

Multi-scale context aggregation by dilated convolutions

Fisher Yu and Vladlen Koltun. Multi-scale context aggregation by dilated convolutions. In ICLR, 2015. 38, 39, 41, 121

2015
[292]

Deep layer aggregation

Fisher Yu, Dequan Wang, Evan Shelhamer, and Trevor Darrell. Deep layer aggregation. In CVPR, 2018. 123

2018
[293]

PoseCNN: A convo- lutional neural network for6D object pose estimation in cluttered scenes

Xiang Yu, Tanner Schmidt, Venkatraman Narayanan, and Dieter Fox. PoseCNN: A convo- lutional neural network for6D object pose estimation in cluttered scenes. InRSS, 2018. 1, 44, 61

2018
[294]

Learning enriched features for fast image restoration and enhancement

Syed Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Khan, Ming-Hsuan Yang, and Ling Shao. Learning enriched features for fast image restoration and enhancement. TPAMI, 2022. 153

2022
[295]

Towards robust3D robot perceptioninurbanenvironments: TheUTCampusObjectDataset(CODa)

ArthurZhang,ChaitanyaEranki,ChristinaZhang,RaymondHong,PranavKalyani,Lochana Kalyanaraman, Arsh Gamare, Maria Esteva, and Joydeep Biswas. Towards robust3D robot perceptioninurbanenvironments: TheUTCampusObjectDataset(CODa). In IROS,2023. 164

2023
[296]

DA-BEV: Depth aware BEV transformer for3D object detection.arXiv preprint 100 arXiv:2302.13002, 2023

Hao Zhang, Hongyang Li, Xingyu Liao, Feng Li, Shilong Liu, Lionel Ni, and Lei Zhang. DA-BEV: Depth aware BEV transformer for3D object detection.arXiv preprint 100 arXiv:2302.13002, 2023. 48, 65

2023 arXiv
[297]

Cameras as rays: Pose estimation via ray diffusion

Jason Zhang, Amy Lin, Moneish Kumar, Tzu-Hsuan Yang, Deva Ramanan, and Shubham Tulsiani. Cameras as rays: Pose estimation via ray diffusion. InICLR, 2024. 65

2024
[298]

Unconstrained salient object detection via proposal subset optimization

Jianming Zhang, Stan Sclaroff, Zhe Lin, Xiaohui Shen, Brian Price, and Radomir Mech. Unconstrained salient object detection via proposal subset optimization. InCVPR, 2016. 9, 16, 17

2016
[2017]

Accessed: 2021-01-18. 10 80

2021
[2021]

26, 34, 36, 53, 55, 56, 57, 75, 119, 121, 122, 126, 152

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.

[1] [1]

http://www.cvlibs.net/datasets/kitti/eval_object.php? obj_benchmark=3d

The KITTI Vision Benchmark Suite. http://www.cvlibs.net/datasets/kitti/eval_object.php? obj_benchmark=3d. Accessed: 2022-07-03. 18, 35

2022

[2] [2]

Augmentedrealitymeetscomputervision: Efficientdatagenerationforurbandrivingscenes

Hassan Alhaĳa, Siva Mustikovela, Lars Mescheder, Andreas Geiger, and Carsten Rother. Augmentedrealitymeetscomputervision: Efficientdatagenerationforurbandrivingscenes. ĲCV, 2018. 1, 6, 25, 44, 61

2018

[3] [3]

Learningdetectionwithdiverseproposals

SamanehAzadi,JiashiFeng,andTrevorDarrell. Learningdetectionwithdiverseproposals. InCVPR, 2017. 9, 16, 17

2017

[4] [4]

VD3D: Taming large video diffusion transformers for 3D camera control.arXiv preprint arXiv:2407.12781, 2024

Sherwin Bahmani, Ivan Skorokhodov, Aliaksandr Siarohin, Willi Menapace, Guocheng Qian, Michael Vasilkovsky, Hsin-Ying Lee, Chaoyang Wang, Jiaxu Zou, Andrea Tagliasac- chi, David Lindell, and Sergey Tulyakov. VD3D: Taming large video diffusion transformers for 3D camera control.arXiv preprint arXiv:2407.12781, 2024. 63, 65

arXiv 2024

[5] [5]

MonoFENet: Monocular3D object detection with feature enhancement networks.IEEE Transactions on Image Processing, 2019

Wentao Bao, Bin Xu, and Zhenzhong Chen. MonoFENet: Monocular3D object detection with feature enhancement networks.IEEE Transactions on Image Processing, 2019. 9

2019

[6] [6]

SemanticKITTI: A dataset for semantic scene understanding of LiDAR sequences

Jens Behley, Martin Garbade, Andres Milioto, Jan Quenzel, Sven Behnke, Cyrill Stachniss, and Jurgen Gall. SemanticKITTI: A dataset for semantic scene understanding of LiDAR sequences. InICCV, 2019. 54

2019

[7] [7]

Monocular differentiable rendering for self-supervised3D object detection

DenizBeker,HiroharuKato,MihaiAdrianMorariu,TakahiroAndo,ToruMatsuoka,Wadim Kehl, and Adrien Gaidon. Monocular differentiable rendering for self-supervised3D object detection. InECCV, 2020. 20

2020

[8] [8]

Learning with differentiable perturbed optimizers

Quentin Berthet, Mathieu Blondel, Olivier Teboul, Marco Cuturi, Jean-Philippe Vert, and Francis Bach. Learning with differentiable perturbed optimizers. InNeurIPS, 2020. 11

2020