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REVIEW 3 major objections 5 minor 60 references

EVT: Efficient View Transformation for Multi-Modal 3D Object Detection

T0 review · 3 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read EVT fuses LiDAR and camera in BEV space by letting LiDAR features dictate where image features are sampled and refined, yielding state-of-the-art 75.3% NDS on nuScenes without depth estimators or test-time augmentation.

desk verdict A genuinely novel fusion architecture with strong ablations, but the 0.2 NDS SOTA margin is too thin and unreproducible to carry the paper's central claim. read the letter →

arxiv 2411.10715 v4 pith:FRCBBBW4 submitted 2024-11-16 cs.CV

Yongjin Lee , Hyeon-Mun Jeong , Yurim Jeon , Sanghyun Kim This is my paper · ORCID
classification cs.CV
keywords 3Dobjectdetectionmulti-modalfusionbird's-eyeviewtransformationLiDAR-cameraquery-basedtransformernuScenesbenchmarkreal-time
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

The paper proposes EVT, a multi-modal 3D object detector that fuses LiDAR and camera data in Bird's Eye View (BEV) space without relying on depth estimation or transformer encoders for view transformation. It claims that using LiDAR features to guide where and how image features are sampled into BEV, through the ASAP module, removes ray-directional misalignment and yields a cleaner BEV representation than prior explicit-fusion methods. On top of this, the decoder samples features at the predicted corners of objects rather than at their centers and mixes those features using position-aware weights, which the paper says captures object geometry more faithfully. The combined system reports state-of-the-art results on nuScenes: 75.3% NDS and 72.6% mAP on the test set, with real-time inference, and the LiDAR-only variant also beats several prior LiDAR-only detectors. The authors argue this makes efficient, robust LiDAR-camera fusion practical without the brittleness of depth-based lifting.

What carries the argument

The load-bearing machinery is the ASAP view-transformation module, consisting of Adaptive Sampling (AS) and Adaptive Projection (AP). AS predicts, for every BEV grid cell, a set of sampling heights and softmax weights from LiDAR BEV features, projecting those 3D points into the multi-scale image features and pooling with LiDAR-derived weights; AP then predicts a per-cell C×C channel-mixing kernel from LiDAR features and applies it to refine the sampled image BEV map. This replaces both depth estimators and attention-based lifting. The second machinery is the geometry-aware cross-attention in the decoder: corner-aware sampling (Eqs. 10–11) takes the length, width, and yaw from the previous layer's regression head, rotates and offsets sampling points to the box corners, and position-aware feature mixing adds sinusoidal position encodings of the offsets before adaptive channel and spatial mixing. Group-wise mixed query selection initializes queries with group-shared learnable embeddings at heatmap-selected locations, giving each object class group a shared prior.

What would settle it

A direct test is to feed ground-truth box parameters (length, width, yaw) into the corner-aware sampling equations during inference on the nuScenes validation set; if the gap between the reported EVT and a variant using predicted boxes is large, the mechanism is indeed using geometry, while if the ground-truth variant does not further improve NDS, the corner geometry is not the operative factor. A complementary probe is to randomize the predicted boxes' yaw in later decoder layers and measure how much NDS drops, where a large drop would confirm the load-bearing role of intermediate regression quality and a small drop would weaken the paper's claim that corner-aware sampling drives the gain.

Watch

Extended reading notes

Core claim

EVT claims that the standard bottleneck in explicit multi-modal fusion is not fusion itself but the view transformation: depth-based lifting is brittle to depth errors, and query-based lifting wastes computation and leaves ray-directional misalignment. Its central discovery is that LiDAR features can supply all the geometric guidance needed for view transformation, feeding both the 3D sampling heights for each BEV grid cell (Adaptive Sampling) and per-cell channel-mixing kernels (Adaptive Projection), so image features are placed in BEV at the heights where objects actually are, with a per-cell correction for occlusions and empty space. The paper further claims that in the query decoder, sampling features at the corners of the predicted box, aligned with yaw, and then mixing those samples with encoded position offsets substantially improves feature decoding over center-based deformable attention; and that initializing object queries with group-shared learnable embeddings plus heatmap-selected positions outperforms both fully learnable and fully heatmap-based initialization. Together, these changes yield a fused BEV representation and a decoder that are more geometrically grounded, and the paper reports a state-of-the-art 75.3% NDS and 72.6% mAP on the nuScenes test set without ensemble or test-time augmentation, along with a transfer result showing the geometry-aware cross-attention also improves a camera-only detector (StreamPETR).

Load-bearing premise

Corner-aware sampling assumes the bounding-box length, width, and yaw predicted by the previous decoder layer are accurate enough that placing sampling points at those corners highlights the object rather than surrounding background, an assumption the paper mitigates only for the first layer and one that gets riskier for poorly regressed or far-away objects.

Editorial extensions

If this is right

  • If the reported results hold, LiDAR-guided view transformation can match or beat depth-based and attention-based lifting while removing the depth estimator and being cheaper, making real-time multi-modal BEV detection feasible without sacrificing accuracy.
  • Corner-aware sampling tied to the regression head's box estimates suggests that query-based decoders can actively use the evolving geometry of object hypotheses rather than only the query's center position, a principle that could transfer to other query-based detectors entirely.
  • The transfer experiment on StreamPETR indicates that geometry-aware cross-attention is a plug-in that improves camera-only 3D detectors as well, widening the scope beyond LiDAR-camera fusion.
  • Since the EVT-L LiDAR-only variant outperforms several LiDAR-only baselines, the proposed query initialization and decoder components are effective independent of the camera branch, implying the gains are not purely from fusion.

Reading between the lines

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

  • The ASAP design implies a broader principle: in any modality pair where one sensor provides reliable geometry (LiDAR, radar, or depth sensors) and the other provides appearance, the geometric sensor can act as a guide dictating where and how the appearance features are projected, avoiding learned depth altogether; a testable extension would be replacing LiDAR with radar BEV features to see how muc
  • Because corner-aware sampling depends on intermediate box estimates, the method's gains may shrink for objects that are hard to regress early, such as far-away, occluded, or rare classes; an implicit prediction is that per-class gains should correlate with class-wise regression quality, which could be checked on the nuScenes validation set.
  • The reported FPS comparison suggests the fused BEV representation is the main real-time bottleneck, so extending ASAP to a temporal-fusion variant may be a straightforward way to push accuracy further while keeping the efficiency advantage, though that is beyond what the paper demonstrates.
  • Group-wise shared query embeddings imply that the model benefits from a categorical prior shared by similarly sized classes, pointing to a design principle for query initialization in other multi-class detection tasks: group by geometry rather than by semantics alone.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 5 minor

Summary. The paper proposes EVT, a multi-modal (LiDAR-camera) 3D object detector for autonomous driving. Its two main contributions are (1) ASAP, a view transformation module that uses LiDAR guidance to generate adaptive sampling heights and adaptive projection kernels, avoiding depth estimation and transformer-based lifting, and (2) an improved query-based decoder with group-wise mixed query selection and geometry-aware cross-attention, which samples features at predicted bounding-box corners and uses position-aware feature mixing. On the nuScenes benchmark, the paper reports 75.3% NDS and 72.6% mAP on the test set and 74.6% NDS / 72.1% mAP on the validation set with a V2-99 backbone, plus 74.1% NDS / 71.1% mAP with ResNet-50 at 8.3 FPS and 71.7% NDS / 66.4% mAP for a LiDAR-only variant at 12.1 FPS. Ablations on the validation set support the contribution of each component, and the geometry-aware cross-attention is shown to transfer to the camera-only detector StreamPETR.

Significance. If the reported numbers hold, EVT is a well-engineered alternative to depth-based and transformer-based view transformation, with a LiDAR-only baseline that is competitive on its own. The internal ablations (Tables 2–6) are clearly structured, and the transfer experiment to StreamPETR (Table 5) provides a useful sanity check that the proposed cross-attention is not just tuned to the authors' own architecture. The paper is honest about using no model ensemble and no test-time augmentation. However, the central claim of state-of-the-art performance rests on a very thin margin with no statistical support, and the paper neither releases code nor checkpoints, which limits verification. The 'real-time inference speed' claim in the abstract is also not aligned with the reported FPS of the configuration that achieves the headline test result.

major comments (3)
  1. [§4.3, Table 1] The state-of-the-art claim rests on a 0.2 NDS margin over FusionFormer (75.3 vs 75.1) with identical mAP (72.6), and the paper reports no training seeds, no error bars, and no code or checkpoints. In DETR-style detectors, run-to-run variation of ±0.2–0.3 NDS is common, so this margin does not by itself establish superiority. Please provide multiple-seed results (at least on the validation split) or release code/checkpoints, or explicitly temper the state-of-the-art claim.
  2. [Abstract; §1; Fig. 1] The abstract states that 'EVT achieves state-of-the-art performance of 75.3% NDS with real-time inference speed,' but the 75.3% test result is obtained with the V2-99 backbone, whose validation speed is reported as 4.9 FPS on an A100 (Fig. 1), well below a common 10 FPS real-time bar. Only the LiDAR-only EVT-L reaches 12.1 FPS, and the ResNet-50 multi-modal variant is 8.3 FPS. Please report the FPS of the exact configuration that produced the test numbers and define the real-time criterion used.
  3. [§4.1; §4.4; Table 5] The paper provides no code, checkpoints, or training configuration for the StreamPETR experiment (Table 5), and several hyperparameters (e.g., the number of image scales N_s, the group definitions, the query counts) are stated as 'empirically determined' without sensitivity analysis. This makes the central empirical contributions difficult to reproduce or verify, and the absence of any variance estimate weakens the comparison not only for the headline SOTA result but also for the ablations.
minor comments (5)
  1. [Abstract; §1; Contributions] The mAP on the nuScenes test set is reported inconsistently: 72.5 in the introduction and 72.6 in the contributions list and Table 1; please unify.
  2. [§3.1, Eqs. (1), (4), (6)] The notation {Z_i} = Conv(BEV_lidar)(u,v) is unconventional; please specify that the convolution outputs N_h channels per grid cell, and similarly that K_ap in Eq. (6) is a C×C output.
  3. [§3.1, Eqs. (2)–(3)] When a projected sampling point falls outside the image plane, the behavior of bilinear interpolation is not stated; please specify the masking or padding policy.
  4. [Fig. 1 caption; §4.1] The FPS measurements exclude voxelization time; please state this in the main text as well and justify the exclusion, since voxelization is part of the LiDAR preprocessing pipeline and affects end-to-end inference latency.
  5. [Table 4] Rows (e) and (f) are difficult to parse because the table does not clearly indicate which configuration uses AdaMixer feature mixing and which uses position-aware feature mixing; please clarify the table or its caption.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: EVT's components are evaluated against external benchmarks and ablations, and no prediction reduces to a fitted input or self-citation.

full rationale

The derivation chain in EVT is self-contained. The central claim, 75.3% NDS on nuScenes test, is the result of end-to-end training on an external benchmark, not a fitted constant disguised as a prediction. The ASAP module learns sampling heights, weights, and adaptive kernels from LiDAR features, and its contribution is isolated by ablations in Table 2 against a vanilla view transformation baseline. The group-wise mixed query selection is supervised by ground-truth Gaussian heatmaps and compared against fully learnable and fully heatmap-based initializations in Table 3. The corner-aware sampling in Eq. 10 uses the previous transformer layer's box prediction only as a geometric prior for sampling locations; this is an iterative refinement mechanism trained jointly, and the final box output is not equal to the input box by construction. The paper also tests the geometry-aware cross-attention on an independent detector, StreamPETR, in Table 5. There are no load-bearing self-citations, and indeed the reference list contains no prior work by the present authors. The only substantive concerns are external validity and reproducibility: the 0.2 NDS margin over FusionFormer is reported from a single run with no variance estimates or released code, and the test-set result is not accompanied by error bars. Those concerns are about evidence strength, not circularity, and under the stated criteria they do not warrant a nonzero circularity score.

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

The paper introduces no new physical entities. Its free parameters are standard architectural hyperparameters, and its axioms are common assumptions in the field. The main structural reliance is on the accuracy of intermediate box predictions in the decoder.

free parameters (4)
  • Number of adaptive sampling heights per BEV cell (Nh) = 4
    Set empirically; affects the number of projected image sampling points per grid cell.
  • Number of decoder sampling points per query (Np) = 16
    Empirically selected for corner-aware sampling.
  • Group-wise query count = 150 per group, 900 total
    Empirically determined; groups defined following CenterPoint [52].
  • Number of image scales for sampling (Ns) = not explicitly stated
    Multi-scale FPN features are used, but the exact number of scales used in Eq. 2-3 is not specified, a missing detail for reproduction.
assumptions (4)
  • domain assumption Known camera intrinsics and extrinsics for nuScenes are used in the projection (Eq. 2).
    The method assumes accurate sensor calibration, which is provided by the dataset.
  • domain assumption The softmax over adaptive sampling weights (Eq. 4) assumes a convex combination of sampled features.
    This is a modeling choice that may limit expressiveness but is standard for attention-like pooling.
  • domain assumption Corner-aware sampling (Eq. 10-11) assumes the previous layer's box prediction is accurate enough to guide sampling.
    The method relies on intermediate regression outputs; inaccuracies could place sampling points on background.
  • domain assumption The BEV feature map is a fixed 180x180 grid over the ROI of [-54m, 54m] in X and Y.
    This resolution is empirically chosen and affects the granularity of view transformation.

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Cite this review

Pith. "Pith review of EVT: Efficient View Transformation for Multi-Modal 3D Object Detection." pith.science (2026). https://pith.science/paper/FRCBBBW4

@misc{pith2026241110715,
  author       = {Pith},
  title        = {Pith review of: EVT: Efficient View Transformation for Multi-Modal 3D Object Detection},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/FRCBBBW4}},
  note         = {Machine review of arXiv:2411.10715}
}
read the original abstract

Multi-modal sensor fusion in Bird's Eye View (BEV) representation has become the leading approach for 3D object detection. However, existing methods often rely on depth estimators or transformer encoders to transform image features into BEV space, which reduces robustness or introduces significant computational overhead. Moreover, the insufficient geometric guidance in view transformation results in ray-directional misalignments, limiting the effectiveness of BEV representations. To address these challenges, we propose Efficient View Transformation (EVT), a novel 3D object detection framework that constructs a well-structured BEV representation, improving both accuracy and efficiency. Our approach focuses on two key aspects. First, Adaptive Sampling and Adaptive Projection (ASAP), which utilizes LiDAR guidance to generate 3D sampling points and adaptive kernels, enables more effective transformation of image features into BEV space and a refined BEV representation. Second, an improved query-based detection framework, incorporating group-wise mixed query selection and geometry-aware cross-attention, effectively captures both the common properties and the geometric structure of objects in the transformer decoder. On the nuScenes test set, EVT achieves state-of-the-art performance of 75.3% NDS with real-time inference speed.

Figures

Figures reproduced from arXiv: 2411.10715 by the authors.

Figure 1
Figure 1. Performance comparison of EVT and other methods on [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Overall architecture of EVT. Each backbone extracts either image features or LiDAR features. The proposed ASAP module [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Overview of AS and AP. In AS, the LiDAR BEV fea [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: Visualization of projected sampling points for each object. The top row shows the projections of predefined 3D points, and the [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]
Figure 5
Figure 5. Figure 5: Comparison of BEV feature maps from different meth [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]

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Works this paper leans on

60 extracted references · 34 canonical work pages

  1. [1]

    Transfusion: Robust lidar-camera fusion for 3d object detection with transform- ers

    Xuyang Bai, Zeyu Hu, Xinge Zhu, Qingqiu Huang, Yilun Chen, Hongbo Fu, and Chiew-Lan Tai. Transfusion: Robust lidar-camera fusion for 3d object detection with transform- ers. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 1080–1089, 2022. 1, 2, 3, 4, 6, 8

    work page 2022

  2. [2]

    nuscenes: A multi- modal dataset for autonomous driving

    Holger Caesar, Varun Bankiti, Alex H Lang, Sourabh V ora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Gi- ancarlo Baldan, and Oscar Beijbom. nuscenes: A multi- modal dataset for autonomous driving. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 11621–11631, 2020. 6

    2020

  3. [3]

    Bevfusion4d: Learn- ing lidar-camera fusion under bird’s-eye-view via cross- modality guidance and temporal aggregation

    Hongxiang Cai, Zeyuan Zhang, Zhenyu Zhou, Ziyin Li, Wenbo Ding, and Jiuhua Zhao. Bevfusion4d: Learn- ing lidar-camera fusion under bird’s-eye-view via cross- modality guidance and temporal aggregation. arXiv preprint arXiv:2303.17099, 2023. 1, 3, 6

    arXiv 2023

  4. [4]

    Objectfusion: Multi-modal 3d object detection with object-centric fusion

    Qi Cai, Yingwei Pan, Ting Yao, Chong-Wah Ngo, and Tao Mei. Objectfusion: Multi-modal 3d object detection with object-centric fusion. In Proceedings of the IEEE/CVF In- ternational Conference on Computer Vision , pages 18067– 18076, 2023. 6

    work page 2023

  5. [5]

    Futr3d: A unified sensor fusion framework for 3d detection

    Xuanyao Chen, Tianyuan Zhang, Yue Wang, Yilun Wang, and Hang Zhao. Futr3d: A unified sensor fusion framework for 3d detection. Inproceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 172–181,

  6. [6]

    Focal- former3d: focusing on hard instance for 3d object detection

    Yilun Chen, Zhiding Yu, Yukang Chen, Shiyi Lan, An- ima Anandkumar, Jiaya Jia, and Jose M Alvarez. Focal- former3d: focusing on hard instance for 3d object detection. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 8394–8405, 2023. 1, 6

    work page 2023

  7. [7]

    Deformable feature aggregation for dynamic multi-modal 3d object detection

    Zehui Chen, Zhenyu Li, Shiquan Zhang, Liangji Fang, Qin- hong Jiang, and Feng Zhao. Deformable feature aggregation for dynamic multi-modal 3d object detection. In European conference on computer vision , pages 628–644. Springer,

  8. [8]

    Autoalign: pixel-instance feature aggregation for multi- modal 3d object detection.arXiv preprint arXiv:2201.06493,

    Zehui Chen, Zhenyu Li, Shiquan Zhang, Liangji Fang, Qinghong Jiang, Feng Zhao, Bolei Zhou, and Hang Zhao. Autoalign: pixel-instance feature aggregation for multi- modal 3d object detection.arXiv preprint arXiv:2201.06493,

    arXiv

Show all 60 references
  1. [9]

    Li3detr: A li- dar based 3d detection transformer

    Gopi Krishna Erabati and Helder Araujo. Li3detr: A li- dar based 3d detection transformer. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 4250–4259, 2023. 2

    2023

  2. [10]

    Adamixer: A fast-converging query-based object detector

    Ziteng Gao, Limin Wang, Bing Han, and Sheng Guo. Adamixer: A fast-converging query-based object detector. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5364–5373, 2022. 5, 8

    2022

  3. [11]

    Deep residual learning for image recognition

    Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceed- ings of the IEEE conference on computer vision and pattern recognition, pages 770–778, 2016. 2, 5, 7

    2016

  4. [12]

    Fusionformer: A multi-sensory fu- sion in bird’s-eye-view and temporal consistent transformer for 3d object detection

    Chunyong Hu, Hang Zheng, Kun Li, Jianyun Xu, Weibo Mao, Maochun Luo, Lingxuan Wang, Mingxia Chen, Qihao Peng, Kaixuan Liu, et al. Fusionformer: A multi-sensory fu- sion in bird’s-eye-view and temporal consistent transformer for 3d object detection. arXiv preprint arXiv:2309.05257 ,

    arXiv

  5. [13]

    Ea-lss: Edge-aware lift-splat-shot framework for 3d bev ob- ject detection

    Haotian Hu, Fanyi Wang, Jingwen Su, Yaonong Wang, Laifeng Hu, Weiye Fang, Jingwei Xu, and Zhiwang Zhang. Ea-lss: Edge-aware lift-splat-shot framework for 3d bev ob- ject detection. arXiv preprint arXiv:2303.17895, 2023. 6

    2023 arXiv

  6. [14]

    Bevdet4d: Exploit tempo- ral cues in multi-camera 3d object detection

    Junjie Huang and Guan Huang. Bevdet4d: Exploit tempo- ral cues in multi-camera 3d object detection. arXiv preprint arXiv:2203.17054, 2022. 3

    2022 arXiv

  7. [15]

    Bevdet: High-performance multi-camera 3d object de- tection in bird-eye-view

    Junjie Huang, Guan Huang, Zheng Zhu, Yun Ye, and Dalong Du. Bevdet: High-performance multi-camera 3d object de- tection in bird-eye-view. arXiv preprint arXiv:2112.11790,

    arXiv

  8. [16]

    Far3d: Expanding the horizon for surround-view 3d object detec- tion

    Xiaohui Jiang, Shuailin Li, Yingfei Liu, Shihao Wang, Fan Jia, Tiancai Wang, Lijin Han, and Xiangyu Zhang. Far3d: Expanding the horizon for surround-view 3d object detec- tion. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 2561–2569, 2024. 2

    2024

  9. [17]

    Msmdfusion: Fusing lidar and camera at multiple scales with multi-depth seeds for 3d ob- ject detection

    Yang Jiao, Zequn Jie, Shaoxiang Chen, Jingjing Chen, Lin Ma, and Yu-Gang Jiang. Msmdfusion: Fusing lidar and camera at multiple scales with multi-depth seeds for 3d ob- ject detection. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages...

    2023

  10. [18]

    Centermask: Real-time anchor-free instance segmentation

    Youngwan Lee and Jongyoul Park. Centermask: Real-time anchor-free instance segmentation. 2020 IEEE/CVF Confer- ence on Computer Vision and Pattern Recognition (CVPR) , pages 13903–13912, 2019. 2, 5

    2020

  11. [19]

    Dn-detr: Accelerate detr training by intro- ducing query denoising

    Feng Li, Hao Zhang, Shilong Liu, Jian Guo, Lionel M Ni, and Lei Zhang. Dn-detr: Accelerate detr training by intro- ducing query denoising. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages 13619–13627, 2022. 2, 6, 7

    2022

  12. [20]

    Gafusion: Adaptive fusing lidar and camera with multi- ple guidance for 3d object detection

    Xiaotian Li, Baojie Fan, Jiandong Tian, and Huijie Fan. Gafusion: Adaptive fusing lidar and camera with multi- ple guidance for 3d object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 21209–21218, 2024. 1, 6

    2024

  13. [21]

    Unifying voxel-based representation with transformer for 3d object detection

    Yanwei Li, Yilun Chen, Xiaojuan Qi, Zeming Li, Jian Sun, and Jiaya Jia. Unifying voxel-based representation with transformer for 3d object detection. Advances in Neural In- formation Processing Systems, 35:18442–18455, 2022. 1, 2, 3, 6

    2022

  14. [22]

    Bevdepth: Acquisition of reliable depth for multi-view 3d object detec- tion

    Yinhao Li, Zheng Ge, Guanyi Yu, Jinrong Yang, Zengran Wang, Yukang Shi, Jianjian Sun, and Zeming Li. Bevdepth: Acquisition of reliable depth for multi-view 3d object detec- tion. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 1477–1485, 2023. 1, 3

    2023

  15. [23]

    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, et al. Fast-bev: A fast and strong bird’s- eye view perception baseline. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024

    2024

  16. [24]

    Bevformer: Learning bird’s-eye-view representation from multi-camera images via spatiotemporal transformers

    Zhiqi Li, Wenhai Wang, Hongyang Li, Enze Xie, Chong- hao Sima, Tong Lu, Yu Qiao, and Jifeng Dai. Bevformer: Learning bird’s-eye-view representation from multi-camera images via spatiotemporal transformers. In European con- ference on computer vision, pages 1–18. Springer, 2022...

    2022

  17. [25]

    Bevfusion: A simple and robust lidar-camera fusion framework

    Tingting Liang, Hongwei Xie, Kaicheng Yu, Zhongyu Xia, Zhiwei Lin, Yongtao Wang, Tao Tang, Bing Wang, and Zhi Tang. Bevfusion: A simple and robust lidar-camera fusion framework. Advances in Neural Information Processing Sys- tems, 35:10421–10434, 2022. 1, 3

    2022

  18. [26]

    Feature pyra- mid networks for object detection

    Tsung-Yi Lin, Piotr Doll ´ar, Ross Girshick, Kaiming He, Bharath Hariharan, and Serge Belongie. Feature pyra- mid networks for object detection. In Proceedings of the IEEE conference on computer vision and pattern recogni- tion, pages 2117–2125, 2017. 5

    2017

  19. [27]

    Sparsebev: High-performance sparse 3d object de- tection from multi-camera videos

    Haisong Liu, Yao Teng, Tao Lu, Haiguang Wang, and Limin Wang. Sparsebev: High-performance sparse 3d object de- tection from multi-camera videos. In Proceedings of the IEEE/CVF International Conference on Computer Vision , pages 18580–18590, 2023. 2, 4, 8

    2023

  20. [28]

    Dab-detr: Dynamic anchor boxes are better queries for detr

    Shilong Liu, Feng Li, Hao Zhang, Xiao Yang, Xianbiao Qi, Hang Su, Jun Zhu, and Lei Zhang. Dab-detr: Dynamic anchor boxes are better queries for detr. arXiv preprint arXiv:2201.12329, 2022. 2

    2022 arXiv

  21. [29]

    Petr: Position embedding transformation for multi-view 3d object detection

    Yingfei Liu, Tiancai Wang, Xiangyu Zhang, and Jian Sun. Petr: Position embedding transformation for multi-view 3d object detection. In European Conference on Computer Vi- sion, pages 531–548. Springer, 2022. 2, 4, 8

    2022

  22. [30]

    Petrv2: A unified framework for 3d perception from multi-camera images

    Yingfei Liu, Junjie Yan, Fan Jia, Shuailin Li, Aqi Gao, Tian- cai Wang, and Xiangyu Zhang. Petrv2: A unified framework for 3d perception from multi-camera images. InProceedings of the IEEE/CVF International Conference on Computer Vi- sion, pages 3262–3272, 2023. 2, 4, 8

    2023

  23. [31]

    Bevfusion: Multi- task multi-sensor fusion with unified bird’s-eye view repre- sentation

    Zhijian Liu, Haotian Tang, Alexander Amini, Xinyu Yang, Huizi Mao, Daniela L Rus, and Song Han. Bevfusion: Multi- task multi-sensor fusion with unified bird’s-eye view repre- sentation. In 2023 IEEE international conference on robotics and automation (ICRA) , pages 2774–2781. ...

    2023

  24. [32]

    Decoupled weight decay regularization

    Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. In ICLR, 2019. 6

    2019

  25. [33]

    Detr4d: Direct multi-view 3d object detection with sparse attention

    Zhipeng Luo, Changqing Zhou, Gongjie Zhang, and Shi- jian Lu. Detr4d: Direct multi-view 3d object detection with sparse attention. arXiv preprint arXiv:2212.07849, 2022. 2

    2022 arXiv

  26. [34]

    Lift, splat, shoot: Encod- ing images from arbitrary camera rigs by implicitly unpro- jecting to 3d

    Jonah Philion and Sanja Fidler. Lift, splat, shoot: Encod- ing images from arbitrary camera rigs by implicitly unpro- jecting to 3d. In Computer Vision–ECCV 2020: 16th Euro- pean Conference, Glasgow, UK, August 23–28, 2020, Pro- ceedings, Part XIV 16 , pages 194–210. Springer,...

    2020

  27. [35]

    Categorical depth distribution network for monocular 3d object detection

    Cody Reading, Ali Harakeh, Julia Chae, and Steven L Waslander. Categorical depth distribution network for monocular 3d object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8555–8564, 2021. 3

    2021

  28. [36]

    Focal loss for dense ob- ject detection

    T-YLPG Ross and GKHP Doll ´ar. Focal loss for dense ob- ject detection. In proceedings of the IEEE conference on computer vision and pattern recognition, pages 2980–2988,

  29. [37]

    Cyclical learning rates for training neural networks

    Leslie N Smith. Cyclical learning rates for training neural networks. In 2017 IEEE winter conference on applications of computer vision (WACV), pages 464–472. IEEE, 2017. 6

    2017

  30. [38]

    Attention is all you need

    Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszko- reit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in neural information processing systems, 30, 2017. 5, 8

    2017

  31. [39]

    Pointpainting: Sequential fusion for 3d object de- tection

    Sourabh V ora, Alex H Lang, Bassam Helou, and Oscar Bei- jbom. Pointpainting: Sequential fusion for 3d object de- tection. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 4604–4612,

  32. [40]

    Unitr: A unified and efficient multi-modal transformer for bird’s-eye-view repre- sentation

    Haiyang Wang, Hao Tang, Shaoshuai Shi, Aoxue Li, Zhen- guo Li, Bernt Schiele, and Liwei Wang. Unitr: A unified and efficient multi-modal transformer for bird’s-eye-view repre- sentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 6792–680...

    2023

  33. [41]

    Jian Wang, Fan Li, and Haixia Bi. Gaussian focal loss: Learning distribution polarized angle prediction for rotated object detection in aerial images.IEEE Transactions on Geo- science and Remote Sensing, 60:1–13, 2022. 6

    2022

  34. [42]

    Exploring object-centric temporal modeling for efficient multi-view 3d object detection

    Shihao Wang, Yingfei Liu, Tiancai Wang, Ying Li, and Xi- angyu Zhang. Exploring object-centric temporal modeling for efficient multi-view 3d object detection. In Proceedings of the IEEE/CVF International Conference on Computer Vi- sion, pages 3621–3631, 2023. 2, 8

    2023

  35. [43]

    Detr3d: 3d object detection from multi-view images via 3d-to-2d queries

    Yue Wang, Vitor Campanholo Guizilini, Tianyuan Zhang, Yilun Wang, Hang Zhao, and Justin Solomon. Detr3d: 3d object detection from multi-view images via 3d-to-2d queries. In Conference on Robot Learning, 2021. 2

    2021

  36. [44]

    Mv2dfusion: Leveraging modality-specific object semantics for multi-modal 3d detection

    Zitian Wang, Zehao Huang, Yulu Gao, Naiyan Wang, and Si Liu. Mv2dfusion: Leveraging modality-specific object semantics for multi-modal 3d detection. arXiv preprint arXiv:2408.05945, 2024. 2, 4, 8

    2024 arXiv

  37. [45]

    Sparsefusion: Fusing multi-modal sparse rep- resentations for multi-sensor 3d object detection

    Yichen Xie, Chenfeng Xu, Marie-Julie Rakotosaona, Patrick Rim, Federico Tombari, Kurt Keutzer, Masayoshi Tomizuka, and Wei Zhan. Sparsefusion: Fusing multi-modal sparse rep- resentations for multi-sensor 3d object detection. InProceed- ings of the IEEE/CVF International Confer...

    2023

  38. [46]

    Cross modal trans- former: Towards fast and robust 3d object detection

    Junjie Yan, Yingfei Liu, Jianjian Sun, Fan Jia, Shuailin Li, Tiancai Wang, and Xiangyu Zhang. Cross modal trans- former: Towards fast and robust 3d object detection. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 18268–18278, 2023. 1, 2, 4, 6, 8

    2023

  39. [47]

    Second: Sparsely embed- ded convolutional detection

    Yan Yan, Yuxing Mao, and Bo Li. Second: Sparsely embed- ded convolutional detection. Sensors, 18(10):3337, 2018. 6

    2018

  40. [48]

    Bevformer v2: Adapting modern image backbones to bird’s-eye-view recognition via perspective su- pervision

    Chenyu Yang, Yuntao Chen, Hao Tian, Chenxin Tao, Xizhou Zhu, Zhaoxiang Zhang, Gao Huang, Hongyang Li, Yu Qiao, Lewei Lu, et al. Bevformer v2: Adapting modern image backbones to bird’s-eye-view recognition via perspective su- pervision. In Proceedings of the IEEE/CVF Conference...

    2023

  41. [49]

    Deepinteraction: 3d object detection via modality interaction

    Zeyu Yang, Jiaqi Chen, Zhenwei Miao, Wei Li, Xiatian Zhu, and Li Zhang. Deepinteraction: 3d object detection via modality interaction. Advances in Neural Information Pro- cessing Systems, 35:1992–2005, 2022. 1, 2, 6

    1992

  42. [50]

    Deepinteraction++: Multi-modality interaction for autonomous driving

    Zeyu Yang, Nan Song, Wei Li, Xiatian Zhu, Li Zhang, and Philip HS Torr. Deepinteraction++: Multi-modality interaction for autonomous driving. arXiv preprint arXiv:2408.05075, 2024. 1, 2

    2024 arXiv

  43. [51]

    Is-fusion: Instance-scene collaborative fusion for multimodal 3d ob- ject detection

    Junbo Yin, Jianbing Shen, Runnan Chen, Wei Li, Ruigang Yang, Pascal Frossard, and Wenguan Wang. Is-fusion: Instance-scene collaborative fusion for multimodal 3d ob- ject detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1490...

    2024

  44. [52]

    Center- based 3d object detection and tracking

    Tianwei Yin, Xingyi Zhou, and Philipp Krahenbuhl. Center- based 3d object detection and tracking. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 11784–11793, 2021. 5

    2021

  45. [53]

    Multi- modal virtual point 3d detection

    Tianwei Yin, Xingyi Zhou, and Philipp Kr ¨ahenb¨uhl. Multi- modal virtual point 3d detection. Advances in Neural Infor- mation Processing Systems, 34:16494–16507, 2021. 1, 6

    2021

  46. [54]

    Dino: Detr with improved denoising anchor boxes for end-to-end object detection

    Hao Zhang, Feng Li, Shilong Liu, Lei Zhang, Hang Su, Jun Zhu, Lionel M Ni, and Heung-Yeung Shum. Dino: Detr with improved denoising anchor boxes for end-to-end object detection. arXiv preprint arXiv:2203.03605, 2022. 2, 4

    2022 arXiv

  47. [55]

    Sparselif: High-performance sparse lidar- camera fusion for 3d object detection

    Hongcheng Zhang, Liu Liang, Pengxin Zeng, Xiao Song, and Zhe Wang. Sparselif: High-performance sparse lidar- camera fusion for 3d object detection. In European Confer- ence on Computer Vision, pages 109–128. Springer, 2024. 2, 4, 6, 8

    2024

  48. [56]

    Simplebev: Improved lidar-camera fusion architecture for 3d object detection

    Yun Zhao, Zhan Gong, Peiru Zheng, Hong Zhu, and Shaohua Wu. Simplebev: Improved lidar-camera fusion architecture for 3d object detection. arXiv preprint arXiv:2411.05292 ,

    arXiv

  49. [57]

    V oxelnet: End-to-end learning for point cloud based 3d object detection

    Yin Zhou and Oncel Tuzel. V oxelnet: End-to-end learning for point cloud based 3d object detection. In Proceedings of the IEEE conference on computer vision and pattern recog- nition, pages 4490–4499, 2018. 5

    2018

  50. [58]

    Centerformer: Center-based transformer for 3d object detection

    Zixiang Zhou, Xiangchen Zhao, Yu Wang, Panqu Wang, and Hassan Foroosh. Centerformer: Center-based transformer for 3d object detection. In European Conference on Com- puter Vision, pages 496–513. Springer, 2022. 2, 4, 8

    2022

  51. [59]

    Class-balanced grouping and sampling for point cloud 3d object detection

    Benjin Zhu, Zhengkai Jiang, Xiangxin Zhou, Zeming Li, and Gang Yu. Class-balanced grouping and sampling for point cloud 3d object detection. arXiv preprint arXiv:1908.09492,

    1908 arXiv

  52. [60]

    Deformable detr: Deformable transform- ers for end-to-end object detection

    Xizhou Zhu, Weijie Su, Lewei Lu, Bin Li, Xiaogang Wang, and Jifeng Dai. Deformable detr: Deformable transform- ers for end-to-end object detection. ArXiv, abs/2010.04159,

    2010 arXiv

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.