Pith. sign in

REVIEW 3 major objections 5 minor 1 cited by

LONG3R: Long Sequence Streaming 3D Reconstruction

T0 review · 3 major / 5 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 LONG3R claims a recurrent memory with attention gating and adaptive voxel pruning keeps streaming 3D reconstruction accurate and real-time on 100–200 frame sequences.

desk verdict A well-engineered streaming 3D reconstruction system whose long-sequence gains over causal baselines are confounded by a one-frame lookahead in the decoder. read the letter →

arxiv 2507.18255 v1 pith:FS2OOL4U submitted 2025-07-24 cs.CV

Zhuoguang Chen , Minghui Qin , Tianyuan Yuan , Zhe Liu , Hang Zhao This is my paper · ORCID
classification cs.CV
keywords streaming3Dreconstructionpointmapregressionspatio-temporalmemorygatingadaptivevoxelpruningrecurrentnetworklong-sequencedriftreal-timeinference
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

LONG3R claims that streaming 3D reconstruction from a monocular video stream can remain accurate and real-time even as sequences grow to hundreds of frames, if the network keeps a 3D memory that discards redundant scene tokens instead of accumulating them. The paper proposes a recurrent pointmap model: each new frame attends to a gated subset of memory, a dual-source decoder alternates between the next frame and the relevant memory, and a 3D spatio-temporal memory prunes tokens by voxel while adapting voxel size to scene scale. A two-stage curriculum training starts with 5-frame clips and fine-tunes on 10 and 32 frames to teach long-range memory use. The reported result is lower drift and better pointmap accuracy than Spann3R and CUT3R on Replica sequences of 100 and 200 frames, lower camera pose error on 7Scenes, TUM, and ScanNet, and about 21 FPS on one RTX 3090 GPU. If true, it gives robots and AR systems dense geometry from long image streams without offline optimization and without the drift that accumulates in shorter-memory streamers.

What carries the argument

The load-bearing object is the 3D spatio-temporal memory: a key-value bank of historical decoder tokens, split into a short-term window $[t-K, t-1]$ and a long-term voxelized part with a fixed token budget of 3000. Its two nonstandard pieces are the attention-based memory gate and the adaptive voxel pruning rule. The gate keeps any memory entry $s$ whose maximum attention weight over the current frame's tokens exceeds a threshold $\tau = 5\times 10^{-4}$. The pruning groups tokens by 3D position using the adaptive voxel size $v_{\text{scene}} = \frac{1}{t-1}\sum_{j=1}^{t-1} v_{\text{img},j}$ with $v_{\text{img}} = \min_i d_i$, where $d_i = \frac{1}{8}\sum_{j\in N(i)} \| \mathbf{P}_i - \mathbf{P}_j \|_2$ is a token's mean 3D distance to its eight neighbors in the image plane, and keeps only the highest-attended token per voxel. The dual-source refined decoder then alternates a PairwiseBlock (between current refined tokens and next-frame coarse tokens) and a MemoryBlock (between current refined tokens and the gated memory), which the paper argues keeps the two feature spaces aligned and avoids the information loss of concatenating them.

What would settle it

On the Replica200 setting, run LONG3R with the per-scene 3D box size from Equation 8 doubled and halved and compare accuracy, completeness, and retained-memory count; if accuracy barely changes, that adaptive rule is not load-bearing. A sharper test is to replay frames while disabling pruning only for regions whose tokens were discarded, and check whether revisit errors in those regions grow.

Watch

Extended reading notes

Core claim

The central claim is that a recurrent pointmap model can handle long image streams with near-constant memory by representing scene history as a 3D spatio-temporal memory that is pruned every step. The memory is a bank of decoder keys and values; a short-term window keeps recent frames, while a long-term 3D spatial memory groups older tokens into voxels in the predicted 3D positions and retains only the token with the highest accumulated attention weight per voxel. The voxel size is set adaptively: each token's mean 3D distance to its eight image-plane neighbors is computed, the smallest such distance gives the image voxel size, and the scene voxel size is the average over frames. An attention-based gate first discards memory tokens no current-frame token attends to, and the dual-source refined decoder interleaves cross-attention to the next frame's coarse features with cross-attention to the retained memory. The authors argue this design controls cumulative drift because long-range spatio-temporal context is repeatedly reused rather than attended once, and they report that on Replica200 the full memory design drops mean accuracy from 65.75 cm (with no 3D spatial memory) to 11.93 cm while keeping the method within about 21 FPS.

Load-bearing premise

The whole result depends on the assumption that keeping only the single best-matching memory token inside each adjustable 3D box still preserves the geometry a later frame needs; if the box size is wrong or the matching signal is uninformative, the speed gain becomes reconstruction error.

Editorial extensions

If this is right

  • If LONG3R's claims hold, streaming dense reconstruction no longer needs to trade sequence length for accuracy: 100–200 frame monocular sequences can be processed at about 21 FPS on a single RTX 3090 while staying competitive with offline multi-view methods on precision.
  • Memory gating alone reduces the number of stored tokens by about 27% on 7Scenes and raises throughput from 18.0 to 21.4 FPS without degrading accuracy, so the pruning is a speed feature as well as a memory feature.
  • The controlled Replica ablation implies the long-term 3D spatial memory, not the decoder or gating alone, is what makes long sequences work: dropping it raises mean accuracy from 11.93 cm to 65.75 cm on Replica200.
  • The interleaved dual-source decoder consistently beats a concatenated variant, with the gap widening on Replica200 (mean accuracy 11.93 cm vs 29.52 cm), consistent with the claim that feature-space alignment underlies the improvement.
  • Lower ATE on ScanNet (6.44 cm vs 9.83 cm for Spann3R and 14.27 cm for CUT3R) indicates the memory design reduces pose drift, not just pointmap noise.

Reading between the lines

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

  • One could test whether making the voxel size local rather than scene-global improves far-field geometry, since the global average $v_{\text{scene}}$ is dominated by dense near-camera tokens and may over-prune distant regions.
  • The attention-threshold gate is purely heuristic; a learned relevance scorer might allow a finer accuracy-versus-compute trade-off, and the reported 27% token reduction suggests there is headroom in both directions.
  • The same voxel-and-attention pruning recipe could transfer to other recurrent dense predictors such as depth or occupancy streaming, where historical tokens also accumulate in 3D and become redundant; the paper does not claim this.
  • If the reported pose accuracy holds on longer trajectories than the evaluated benchmarks, loop-closure-free online reconstruction could become practical for robotic navigation, since the method achieves real-time speed without bundle adjustment.
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.

Referee Report

3 major / 5 minor

Summary. The paper presents LONG3R, a recurrent streaming 3D reconstruction model that combines a memory gating module, a dual-source refined decoder, a 3D spatio-temporal memory with adaptive-voxel pruning, and a two-stage curriculum training procedure. The experiments compare against Spann3R, CUT3R, and other DUSt3R-based methods on 7Scenes, NRGBD, Replica100/200, and camera pose benchmarks, reporting better accuracy and completion on long sequences with about 21 FPS on a single RTX 3090 GPU.

Significance. If the reported results hold under a fair causal comparison, the adaptive 3D spatial memory is a useful contribution to bounded-memory streaming reconstruction, and the two-stage curriculum is a practical training recipe. The paper evaluates on held-out datasets and includes component ablations, which is a strength. However, the architecture's use of frame t+1 during decoding is a major confound for the streaming comparisons, and the numerical evidence lacks uncertainty quantification. The central claim of outperforming state-of-the-art streaming methods is therefore not yet established.

major comments (3)
  1. [§3.3, Eq. (7); Fig. 2; Tables 1–3] The Dual-Source Refined Decoder uses F^c_{t+1,i-1} as the second input of every odd PairwiseBlock (Eq. 7), and the Fig. 2 caption states that the decoder interacts with features from t+1 and ultimately generates the pointmap for t. This gives LONG3R access to the next frame, so the method is not causal. Spann3R and CUT3R, the primary streaming baselines, are recurrent causal streamers that do not receive future frames. The paper provides no ablation that removes or masks the t+1 branch, and Sections 4.1–4.3 do not disclose the resulting one-frame output latency. Consequently, the Replica100/200 and pose improvements in Tables 2 and 3 could be due to lookahead or temporal smoothing rather than to memory gating, 3D spatial memory, or curriculum training. The FPS comparison is also not apples-to-apples if LONG3R outputs pointmap t only after frame t+1 arrives. I request a causal ablation (for example, replacing F^c_{t+1} with F^c_t or using an independent stream for the current frame) and a baseline comparison with equivalent lookahead, plus a statement of output latency.
  2. [§4.2, Tables 1 and 2] No error bars, confidence intervals, or significance tests are reported for any reconstruction metric, and the improvements over Spann3R are not uniform. In Table 1 on NRGBD the mean completion error is worse for Ours (3.11 cm vs 2.91 cm) and normal consistency is slightly lower (77.56 vs 77.75); on Replica200 the normal consistency gap is 68.67 vs 68.56, essentially a tie. Only the Replica accuracy and completion differences are large enough to be obviously meaningful without variance estimates. Please report per-sequence standard deviations or significance tests, and state how many scenes are used for each mean.
  3. [§3.4, Eq. (8)] The adaptive voxel size v_img is defined as the minimum mean neighbor distance across all tokens, which can be dominated by a single outlier token; the paper calls this selection optimal without justification. The pruning rule keeps the token with the highest accumulated attention weight per voxel, but no ablation compares this selection rule against random retention or against a mean or median voxel-size definition. Since the memory-efficiency/accuracy tradeoff is a central claim, please add sensitivity experiments for the voxel-size statistic and the pruning criterion.
minor comments (5)
  1. [§3.4, Eq. (8)] In Eq. (8), v_img is used both as the per-frame image voxel size v_img,j and as the scene-level voxel size; please unify the notation to avoid ambiguity.
  2. [§4.1] The memory gating threshold tau = 5e-4, the short-term window K = 10, and the 3000-token long-term budget are fixed without sensitivity analysis; a short paragraph on their effect, or a reference to a supplementary ablation, would strengthen the paper.
  3. [§4.1–4.2] The paper does not state whether the Spann3R and CUT3R numbers are produced by the authors using official checkpoints or taken from prior publications; please specify the evaluation protocol.
  4. [Table 4] The FPS column placement in Table 4 is easy to misread because the w/o Gating row has eight numeric values before the FPS value; please align the FPS values clearly and specify whether FPS includes encoding, memory update, and the pointmap head.
  5. [References] References [29] and [30] appear to cite the same MASt3R paper in two versions; consider citing the published version once.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity found; the method's gains are evaluated on held-out datasets and no prediction reduces to a fitted input by construction.

full rationale

LONG3R is an empirical learning-based method rather than a derivation from first principles, so the main circularity patterns do not apply. The model is trained on a mixture of six datasets and then evaluated on held-out datasets (7Scenes, NRGBD, Replica, TUM, ScanNet), meaning the reported reconstruction and pose numbers are not equivalent to the training loss or to any fitted parameter by construction. The cited prior methods (Spann3R, CUT3R, DUSt3R, MASt3R) are external baselines, and no load-bearing claim rests on a self-citation chain or an imported uniqueness theorem. Hyperparameters such as the attention threshold, memory window, and token budget are hand-chosen, but selecting hyperparameters is not the same as fitting a parameter to the benchmark and then renaming the fit a prediction. One legitimate concern is that the Dual-Source Refined Decoder uses next-frame features in Eq. (7), giving LONG3R a lookahead that the causal baselines lack; however, this is an experimental-fairness and correctness issue, not a circularity, because it does not make the output equivalent to the input. No specific reduction of a claimed result to its own inputs or to a self-citation was found.

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

The central claims rest on learned network weights and on several hand-set hyperparameters and heuristic design choices. The free parameters are the attention threshold, memory window, token budget, and loss weighting. The axioms are the relevance assumption behind attention gating, the redundancy assumption behind voxel pruning, the heuristic voxel-size rule, and the training-data generalization assumption. No new physical or mathematical entities are introduced.

free parameters (4)
  • attention threshold tau = 5e-4
    Fixed threshold in Eq. 5 for discarding memory tokens; no sensitivity analysis is reported, and it directly controls which memories are retained.
  • short-term memory window K = 10 frames
    Set in implementation details; controls how many recent frames are kept in short-term temporal memory, with no ablation.
  • long-term memory budget = 3000 tokens
    Fixed capacity for the 3D spatial memory in implementation details; influences memory efficiency and reconstruction quality, with no ablation.
  • loss weights for Lconf and Lscale = 1.0 each
    Equation 9 sums the two losses with equal weight; no tuning or ablation is reported for this weighting.
assumptions (4)
  • domain assumption Attention weights from current-frame features to memory keys indicate spatial and temporal relevance of memory tokens.
    The memory gating mechanism (Eqs. 3-6) relies on this assumption to decide which memories to keep; no evidence is provided that max attention weight is a reliable relevance signal.
  • domain assumption Retaining only the highest-attention token per voxel preserves the geometric information needed for reconstruction.
    The 3D spatial memory pruning in Sec. 3.4 assumes within-voxel token redundancy is safely removable; this is a heuristic with no geometric error analysis.
  • ad hoc to paper The minimum mean neighbor distance in 3D (Eq. 8) is the appropriate voxel size for balancing memory use and accuracy.
    The adaptive voxel size strategy is introduced specifically for this method; it is motivated intuitively but not derived or justified with ablations over voxel-size choices.
  • domain assumption The training mixture of six datasets provides sufficient metric-scale supervision for generalization to the evaluation datasets.
    The paper follows Spann3R's training recipe; the assumption is that scale and geometry priors transfer from Habitat, ARKitScenes, BlendedMVS, ScanNet++, CO3D-v2, and ScanNet to unseen 7Scenes, NRGBD, Replica, TUM, and ScanNet evaluations.

how reviews work

0 comments
Cite this review

Pith. "Pith review of LONG3R: Long Sequence Streaming 3D Reconstruction." pith.science (2026). https://pith.science/paper/FS2OOL4U

@misc{pith2026250718255,
  author       = {Pith},
  title        = {Pith review of: LONG3R: Long Sequence Streaming 3D Reconstruction},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/FS2OOL4U}},
  note         = {Machine review of arXiv:2507.18255}
}
read the original abstract

Recent advancements in multi-view scene reconstruction have been significant, yet existing methods face limitations when processing streams of input images. These methods either rely on time-consuming offline optimization or are restricted to shorter sequences, hindering their applicability in real-time scenarios. In this work, we propose LONG3R (LOng sequence streaming 3D Reconstruction), a novel model designed for streaming multi-view 3D scene reconstruction over longer sequences. Our model achieves real-time processing by operating recurrently, maintaining and updating memory with each new observation. We first employ a memory gating mechanism to filter relevant memory, which, together with a new observation, is fed into a dual-source refined decoder for coarse-to-fine interaction. To effectively capture long-sequence memory, we propose a 3D spatio-temporal memory that dynamically prunes redundant spatial information while adaptively adjusting resolution along the scene. To enhance our model's performance on long sequences while maintaining training efficiency, we employ a two-stage curriculum training strategy, each stage targeting specific capabilities. Experiments demonstrate that LONG3R outperforms state-of-the-art streaming methods, particularly for longer sequences, while maintaining real-time inference speed. Project page: https://zgchen33.github.io/LONG3R/.

Figures

Figures reproduced from arXiv: 2507.18255 by the authors.

Figure 1
Figure 1. (Top): Overview of our LONG3R framework, which [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Method Overview. (a) Illustrates the overall architecture, where image features F I t first interact with F I t−1 in the Coarse Decoder to generate F c t , after which a memory-gating module filters irrelevant entries from the spatio-temporal memory Fmem. The Dual-Source Refined Decoder subsequently interacts with both the filtered memory and features from t + 1, ultimately generating the pointmap t. (b) Details the… view at source ↗
Figure 3
Figure 3. 3D Spatial Memory Pruning. Memory tokens are grouped into voxels based on 3D positions (illustrated here in a 2D simplified view), with only the token having the highest ac￾cumulated attention weight retained per voxel. Darker blue dots indicate higher attention weights. spatial memory maintains an overall spatial representation while avoiding redundant tokens. This memory design ef￾ficiently captures essential spat… view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: Qualitative comparisons. We present a comparison of reconstruction results on Office-06 and Office-09 from the 7Scenes [52] dataset with the Spann3R [63] and CUT3R [67] methods. In comparison with other online reconstruction methods, our approach achieves superior spat…
Figure 5
Figure 5. Figure 5: Qualitative comparisons. We present a comparison of reconstruction results on Office-0 from the Replica [54] dataset with the Spann3R [63] and CUT3R [67] methods. During streaming observations within the same scene, existing methods suffer from increasing ambiguity due…
Figure 6
Figure 6. Figure 6: Memory number comparison. This figure illustrates the number of memory tokens before (all memory) and after (rel￾evant memory) passing through the memory gating module. racy due to global alignment effectively mitigating drift, al￾beit at the cost of significantly lowe…
Figure 7
Figure 7. Figure 7: Dual-Source Decoder Comparison. This figure com￾pares the interleaved and concatenated architectures of the Dual￾Source Refined Decoder. As shown in Tab. 3, our method significantly out￾performs Spann3R and CUT3R on static scene datasets, namely 7Scenes and ScanNet. De…

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. GeoWorldAD: Geometry World Action Model for Autonomous Driving

    cs.RO 2026-07 conditional novelty 6.0 of 10

    Grounding an autonomous-driving action model in ego-aligned multi-scale 3D geometry and latent future-geometry tokens improves NAVSIM closed-loop PDMS/EPDMS over prior geometry- and world-model-based planners.

Reference graph

Works this paper leans on

89 extracted references · 27 canonical work pages · cited by 1 Pith paper

  1. [1]

    Large-scale data for multiple-view stereopsis

    Henrik Aanæs, Rasmus Ramsbøl Jensen, George V ogiatzis, Engin Tola, and Anders Bjorholm Dahl. Large-scale data for multiple-view stereopsis. International Journal of Computer Vision, 120:153–168, 2016. 1

    2016

  2. [2]

    Bundle adjustment in the large

    Sameer Agarwal, Noah Snavely, Steven M Seitz, and Richard Szeliski. Bundle adjustment in the large. In Proc. European Conf. on Computer Vision (ECCV), pages 29–42. Springer, 2010. 1

    2010

  3. [3]

    Neural rgb-d surface reconstruction

    Dejan Azinovi ´c, Ricardo Martin-Brualla, Dan B Goldman, Matthias Nießner, and Justus Thies. Neural rgb-d surface reconstruction. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 6290– 6301, 2022. 5, 7, 8

    2022

  4. [4]

    Arkitscenes: A diverse real-world dataset for 3d indoor scene understanding using mobile rgb-d data

    Gilad Baruch, Zhuoyuan Chen, Afshin Dehghan, Tal Dimry, Yuri Feigin, Peter Fu, Thomas Gebauer, Brandon Joffe, Daniel Kurz, Arik Schwartz, et al. Arkitscenes: A diverse real-world dataset for 3d indoor scene understanding using mobile rgb-d data. arXiv preprint arXiv:2111.08897, 2021. 6

    arXiv 2021

  5. [5]

    Zoedepth: Zero-shot trans- fer by combining relative and metric depth

    Shariq Farooq Bhat, Reiner Birkl, Diana Wofk, Peter Wonka, and Matthias M ¨uller. Zoedepth: Zero-shot trans- fer by combining relative and metric depth. arXiv preprint arXiv:2302.12288, 2023. 2

    arXiv 2023

  6. [6]

    Past, present, and future of simultaneous localiza- tion and mapping: Toward the robust-perception age

    Cesar Cadena, Luca Carlone, Henry Carrillo, Yasir Latif, Davide Scaramuzza, Jos ´e Neira, Ian Reid, and John J Leonard. Past, present, and future of simultaneous localiza- tion and mapping: Toward the robust-perception age. IEEE Transactions on robotics, 32(6):1309–1332, 2016. 2

    2016

  7. [7]

    Tensorf: Tensorial radiance fields

    Anpei Chen, Zexiang Xu, Andreas Geiger, Jingyi Yu, and Hao Su. Tensorf: Tensorial radiance fields. In Proc. Eu- ropean Conf. on Computer Vision (ECCV) , pages 333–350. Springer, 2022. 2

    2022

  8. [8]

    Leap-vo: Long-term effective any point tracking for visual odometry

    Weirong Chen, Le Chen, Rui Wang, and Marc Pollefeys. Leap-vo: Long-term effective any point tracking for visual odometry. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 19844– 19853, 2024. 2, 7

    2024

Show all 89 references
  1. [9]

    Discrete-continuous optimization for large- scale structure from motion

    David Crandall, Andrew Owens, Noah Snavely, and Dan Huttenlocher. Discrete-continuous optimization for large- scale structure from motion. In Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recog- nition., pages 3001–3008. IEEE, 2011. 1

    2011

  2. [10]

    Scannet: Richly-annotated 3d reconstructions of indoor scenes

    Angela Dai, Angel X Chang, Manolis Savva, Maciej Hal- ber, Thomas Funkhouser, and Matthias Nießner. Scannet: Richly-annotated 3d reconstructions of indoor scenes. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 5828–5839, 2017. 6, 7

    2017

  3. [11]

    Monoslam: Real-time single camera slam

    Andrew J Davison, Ian D Reid, Nicholas D Molton, and Olivier Stasse. Monoslam: Real-time single camera slam. IEEE transactions on pattern analysis and machine intelli- gence, 29(6):1052–1067, 2007. 1, 2

    2007

  4. [12]

    Superpoint: Self-supervised interest point detection and description

    Daniel DeTone, Tomasz Malisiewicz, and Andrew Rabi- novich. Superpoint: Self-supervised interest point detection and description. In Proceedings of the IEEE conference on computer vision and pattern recognition workshops , pages 224–236, 2018. 2

    2018

  5. [13]

    Reloc3r: Large- scale training of relative camera pose regression for general- izable, fast, and accurate visual localization

    Siyan Dong, Shuzhe Wang, Shaohui Liu, Lulu Cai, Qing- nan Fan, Juho Kannala, and Yanchao Yang. Reloc3r: Large- scale training of relative camera pose regression for general- izable, fast, and accurate visual localization. arXiv preprint arXiv:2412.08376, 2024. 2

    2024 arXiv

  6. [14]

    An image is worth 16x16 words: Trans- formers for image recognition at scale

    Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Syl- vain Gelly, et al. An image is worth 16x16 words: Trans- formers for image recognition at scale. arXiv preprint a...

    2010 arXiv

  7. [15]

    Mast3r- sfm: a fully-integrated solution for unconstrained structure- from-motion

    Bardienus Duisterhof, Lojze Zust, Philippe Weinzaepfel, Vincent Leroy, Yohann Cabon, and Jerome Revaud. Mast3r- sfm: a fully-integrated solution for unconstrained structure- from-motion. arXiv preprint arXiv:2409.19152, 2024. 2

    2024 arXiv

  8. [16]

    Simultaneous local- ization and mapping: part i

    Hugh Durrant-Whyte and Tim Bailey. Simultaneous local- ization and mapping: part i. IEEE robotics & automation magazine, 13(2):99–110, 2006. 2

    2006

  9. [17]

    D2- net: A trainable cnn for joint description and detection of local features

    Mihai Dusmanu, Ignacio Rocco, Tomas Pajdla, Marc Polle- feys, Josef Sivic, Akihiko Torii, and Torsten Sattler. D2- net: A trainable cnn for joint description and detection of local features. In Proceedings of the ieee/cvf conference on computer vision and pattern recognition, ...

  10. [18]

    Lsd- slam: Large-scale direct monocular slam

    Jakob Engel, Thomas Sch ¨ops, and Daniel Cremers. Lsd- slam: Large-scale direct monocular slam. In European con- ference on computer vision, pages 834–849. Springer, 2014. 2

    2014

  11. [19]

    Driv3r: Learn- ing dense 4d reconstruction for autonomous driving

    Xin Fei, Wenzhao Zheng, Yueqi Duan, Wei Zhan, Masayoshi Tomizuka, Kurt Keutzer, and Jiwen Lu. Driv3r: Learn- ing dense 4d reconstruction for autonomous driving. arXiv preprint arXiv:2412.06777, 2024. 2

    2024 arXiv

  12. [20]

    Svo: Semidirect visual odometry for monocular and multicamera systems

    Christian Forster, Zichao Zhang, Michael Gassner, Manuel Werlberger, and Davide Scaramuzza. Svo: Semidirect visual odometry for monocular and multicamera systems. IEEE Transactions on robotics, 33(2):249–265, 2016. 2

    2016

  13. [21]

    Plenoxels: Radiance fields without neural networks

    Sara Fridovich-Keil, Alex Yu, Matthew Tancik, Qinhong Chen, Benjamin Recht, and Angjoo Kanazawa. Plenoxels: Radiance fields without neural networks. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 5501–5510, 2022. 2

    2022

  14. [22]

    Accurate, dense, and robust multiview stereopsis

    Yasutaka Furukawa and Jean Ponce. Accurate, dense, and robust multiview stereopsis. IEEE transactions on pattern analysis and machine intelligence , 32(8):1362–1376, 2009. 1

    2009

  15. [23]

    Massively parallel multiview stereopsis by surface normal diffusion

    Silvano Galliani, Katrin Lasinger, and Konrad Schindler. Massively parallel multiview stereopsis by surface normal diffusion. In Proceedings of the IEEE international confer- ence on computer vision, pages 873–881, 2015. 1

    2015

  16. [24]

    Digging into self-supervised monocular 9 depth estimation

    Cl ´ement Godard, Oisin Mac Aodha, Michael Firman, and Gabriel J Brostow. Digging into self-supervised monocular 9 depth estimation. In Proceedings of the IEEE/CVF inter- national conference on computer vision , pages 3828–3838,

  17. [25]

    Metric3d v2: A versatile monocular geomet- ric foundation model for zero-shot metric depth and surface normal estimation

    Mu Hu, Wei Yin, Chi Zhang, Zhipeng Cai, Xiaoxiao Long, Hao Chen, Kaixuan Wang, Gang Yu, Chunhua Shen, and Shaojie Shen. Metric3d v2: A versatile monocular geomet- ric foundation model for zero-shot metric depth and surface normal estimation. IEEE Transactions on Pattern Analys...

    2024

  18. [26]

    2d gaussian splatting for geometrically ac- curate radiance fields

    Binbin Huang, Zehao Yu, Anpei Chen, Andreas Geiger, and Shenghua Gao. 2d gaussian splatting for geometrically ac- curate radiance fields. In ACM SIGGRAPH 2024 conference papers, pages 1–11, 2024. 2

    2024

  19. [27]

    3d gaussian splatting for real-time radiance field rendering

    Bernhard Kerbl, Georgios Kopanas, Thomas Leimk ¨uhler, and George Drettakis. 3d gaussian splatting for real-time radiance field rendering. ACM Trans. Graph., 42(4):139–1,

  20. [28]

    Parallel tracking and map- ping for small ar workspaces

    Georg Klein and David Murray. Parallel tracking and map- ping for small ar workspaces. In 2007 6th IEEE and ACM international symposium on mixed and augmented reality , pages 225–234. IEEE, 2007. 1, 2

    2007

  21. [29]

    Ground- ing image matching in 3d with mast3r

    Vincent Leroy, Yohann Cabon, and J´erˆome Revaud. Ground- ing image matching in 3d with mast3r. In European Confer- ence on Computer Vision, pages 71–91. Springer, 2024. 2, 5, 6, 7

    2024

  22. [30]

    Ground- ing image matching in 3d with mast3r

    Vincent Leroy, Yohann Cabon, and J´erˆome Revaud. Ground- ing image matching in 3d with mast3r. arXiv preprint arXiv:2406.09756, 2024. 1, 2

    2024 arXiv

  23. [31]

    Megadepth: Learning single- view depth prediction from internet photos

    Zhengqi Li and Noah Snavely. Megadepth: Learning single- view depth prediction from internet photos. In Proceed- ings of the IEEE conference on computer vision and pattern recognition, pages 2041–2050, 2018. 2

    2018

  24. [32]

    Slam3r: Real- time dense scene reconstruction from monocular rgb videos

    Yuzheng Liu, Siyan Dong, Shuzhe Wang, Yingda Yin, Yan- chao Yang, Qingnan Fan, and Baoquan Chen. Slam3r: Real- time dense scene reconstruction from monocular rgb videos. arXiv preprint arXiv:2412.09401, 2024. 2

    2024 arXiv

  25. [33]

    Object recognition from local scale-invariant features

    David G Lowe. Object recognition from local scale-invariant features. In Proceedings of the seventh IEEE interna- tional conference on computer vision , pages 1150–1157. Ieee, 1999. 1

    1999

  26. [34]

    Distinctive image features from scale- invariant keypoints

    David G Lowe. Distinctive image features from scale- invariant keypoints. International journal of computer vi- sion, 60:91–110, 2004. 1

    2004

  27. [35]

    Align3r: Aligned monocular depth estimation for dynamic videos

    Jiahao Lu, Tianyu Huang, Peng Li, Zhiyang Dou, Cheng Lin, Zhiming Cui, Zhen Dong, Sai-Kit Yeung, Wenping Wang, and Yuan Liu. Align3r: Aligned monocular depth estimation for dynamic videos. arXiv preprint arXiv:2412.03079, 2024. 2

    2024 arXiv

  28. [36]

    Lora3d: Low-rank self- calibration of 3d geometric foundation models

    Ziqi Lu, Heng Yang, Danfei Xu, Boyi Li, Boris Ivanovic, Marco Pavone, and Yue Wang. Lora3d: Low-rank self- calibration of 3d geometric foundation models. arXiv preprint arXiv:2412.07746, 2024. 2

    2024 arXiv

  29. [37]

    Nerf: Representing scenes as neural radiance fields for view syn- thesis

    Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng. Nerf: Representing scenes as neural radiance fields for view syn- thesis. Communications of the ACM , 65(1):99–106, 2021. 2

    2021

  30. [38]

    Instant neural graphics primitives with a mul- tiresolution hash encoding

    Thomas M ¨uller, Alex Evans, Christoph Schied, and Alexan- der Keller. Instant neural graphics primitives with a mul- tiresolution hash encoding. ACM transactions on graphics (TOG), 41(4):1–15, 2022. 2

    2022

  31. [39]

    Orb-slam: A versatile and accurate monocular slam system

    Raul Mur-Artal, Jose Maria Martinez Montiel, and Juan D Tardos. Orb-slam: A versatile and accurate monocular slam system. IEEE transactions on robotics , 31(5):1147–1163,

  32. [40]

    Mast3r-slam: Real-time dense slam with 3d reconstruction priors

    Riku Murai, Eric Dexheimer, and Andrew J Davison. Mast3r-slam: Real-time dense slam with 3d reconstruction priors. arXiv preprint arXiv:2412.12392, 2024. 2

    2024 arXiv

  33. [41]

    Dtam: Dense tracking and mapping in real-time

    Richard A Newcombe, Steven J Lovegrove, and Andrew J Davison. Dtam: Dense tracking and mapping in real-time. In 2011 international conference on computer vision , pages 2320–2327. IEEE, 2011. 1, 2

    2011

  34. [42]

    Unidepth: Universal monocular metric depth estimation

    Luigi Piccinelli, Yung-Hsu Yang, Christos Sakaridis, Mattia Segu, Siyuan Li, Luc Van Gool, and Fisher Yu. Unidepth: Universal monocular metric depth estimation. In Proceed- ings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10106–10116, 2024. 2

    2024

  35. [43]

    Towards robust monocular depth estimation: Mixing datasets for zero-shot cross-dataset transfer

    Ren ´e Ranftl, Katrin Lasinger, David Hafner, Konrad Schindler, and Vladlen Koltun. Towards robust monocular depth estimation: Mixing datasets for zero-shot cross-dataset transfer. IEEE transactions on pattern analysis and machine intelligence, 44(3):1623–1637, 2020

    2020

  36. [44]

    Vi- sion transformers for dense prediction

    Ren ´e Ranftl, Alexey Bochkovskiy, and Vladlen Koltun. Vi- sion transformers for dense prediction. In Proceedings of the IEEE/CVF international conference on computer vision, pages 12179–12188, 2021. 2

    2021

  37. [45]

    Com- mon objects in 3d: Large-scale learning and evaluation of real-life 3d category reconstruction

    Jeremy Reizenstein, Roman Shapovalov, Philipp Henzler, Luca Sbordone, Patrick Labatut, and David Novotny. Com- mon objects in 3d: Large-scale learning and evaluation of real-life 3d category reconstruction. In Proceedings of the IEEE/CVF international conference on computer vi...

    2021

  38. [46]

    Orb: An efficient alternative to sift or surf

    Ethan Rublee, Vincent Rabaud, Kurt Konolige, and Gary Bradski. Orb: An efficient alternative to sift or surf. In 2011 International conference on computer vision , pages 2564–

    2011

  39. [47]

    Superglue: Learning feature matching with graph neural networks

    Paul-Edouard Sarlin, Daniel DeTone, Tomasz Malisiewicz, and Andrew Rabinovich. Superglue: Learning feature matching with graph neural networks. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 4938–4947, 2020. 2

    2020

  40. [48]

    Habitat: A platform for embodied ai research

    Manolis Savva, Abhishek Kadian, Oleksandr Maksymets, Yili Zhao, Erik Wijmans, Bhavana Jain, Julian Straub, Jia Liu, Vladlen Koltun, Jitendra Malik, et al. Habitat: A platform for embodied ai research. In Proceedings of the IEEE/CVF international conference on computer vision, ...

    2019

  41. [49]

    Structure-from-motion revisited

    Johannes Lutz Sch ¨onberger and Jan-Michael Frahm. Structure-from-motion revisited. In Conference on Com- puter Vision and Pattern Recognition (CVPR), 2016. 1

    2016

  42. [50]

    Structure- from-motion revisited

    Johannes L Schonberger and Jan-Michael Frahm. Structure- from-motion revisited. In Proceedings of the IEEE con- ference on computer vision and pattern recognition , pages 4104–4113, 2016. 1 10

    2016

  43. [51]

    Pixelwise view selection for unstructured multi-view stereo

    Johannes L Sch ¨onberger, Enliang Zheng, Jan-Michael Frahm, and Marc Pollefeys. Pixelwise view selection for unstructured multi-view stereo. In Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Nether- lands, October 11-14, 2016, Proceedings, Part III 14, pag...

    2016

  44. [52]

    Scene co- ordinate regression forests for camera relocalization in rgb-d images

    Jamie Shotton, Ben Glocker, Christopher Zach, Shahram Izadi, Antonio Criminisi, and Andrew Fitzgibbon. Scene co- ordinate regression forests for camera relocalization in rgb-d images. In Proceedings of the IEEE conference on computer vision and pattern recognition , pages 2930...

    2013

  45. [53]

    Photo tourism: exploring photo collections in 3d

    Noah Snavely, Steven M Seitz, and Richard Szeliski. Photo tourism: exploring photo collections in 3d. In ACM transac- tions on graphics (TOG), pages 835–846. ACM, 2006. 1

    2006

  46. [54]

    Julian Straub, Thomas Whelan, Lingni Ma, Yufan Chen, Erik Wijmans, Simon Green, Jakob J. Engel, Raul Mur-Artal, Carl Ren, Shobhit Verma, Anton Clarkson, Mingfei Yan, Brian Budge, Yajie Yan, Xiaqing Pan, June Yon, Yuyang Zou, Kimberly Leon, Nigel Carter, Jesus Briales, Tyler Gi...

    1906 arXiv

  47. [55]

    A benchmark for the evalua- tion of rgb-d slam systems

    J ¨urgen Sturm, Nikolas Engelhard, Felix Endres, Wolfram Burgard, and Daniel Cremers. A benchmark for the evalua- tion of rgb-d slam systems. In 2012 IEEE/RSJ international conference on intelligent robots and systems, pages 573–580. IEEE, 2012. 7

    2012

  48. [56]

    Loftr: Detector-free local feature matching with transformers

    Jiaming Sun, Zehong Shen, Yuang Wang, Hujun Bao, and Xiaowei Zhou. Loftr: Detector-free local feature matching with transformers. In Proceedings of the IEEE/CVF con- ference on computer vision and pattern recognition , pages 8922–8931, 2021. 2

    2021

  49. [57]

    Optimizing the viewing graph for structure-from-motion

    Chris Sweeney, Torsten Sattler, Tobias Hollerer, Matthew Turk, and Marc Pollefeys. Optimizing the viewing graph for structure-from-motion. In Proceedings of the IEEE interna- tional conference on computer vision, pages 801–809, 2015. 1

    2015

  50. [58]

    Ba-net: Dense bundle ad- justment network

    Chengzhou Tang and Ping Tan. Ba-net: Dense bundle ad- justment network. arXiv preprint arXiv:1806.04807, 2018. 2

    2018 arXiv

  51. [59]

    Mv-dust3r+: Single-stage scene reconstruction from sparse views in 2 seconds

    Zhenggang Tang, Yuchen Fan, Dilin Wang, Hongyu Xu, Rakesh Ranjan, Alexander Schwing, and Zhicheng Yan. Mv-dust3r+: Single-stage scene reconstruction from sparse views in 2 seconds. arXiv preprint arXiv:2412.06974, 2024. 2, 5, 6, 7

    2024 arXiv

  52. [60]

    Cnn-slam: Real-time dense monocular slam with learned depth prediction

    Keisuke Tateno, Federico Tombari, Iro Laina, and Nassir Navab. Cnn-slam: Real-time dense monocular slam with learned depth prediction. In Proceedings of the IEEE con- ference on computer vision and pattern recognition , pages 6243–6252, 2017. 2

    2017

  53. [61]

    DROID-SLAM: Deep Visual SLAM for Monocular, Stereo, and RGB-D Cameras

    Zachary Teed and Jia Deng. DROID-SLAM: Deep Visual SLAM for Monocular, Stereo, and RGB-D Cameras. Neural Information Processing Systems, 2021. 2

    2021

  54. [62]

    Bundle adjustment—a mod- ern synthesis

    Bill Triggs, Philip F McLauchlan, Richard I Hartley, and Andrew W Fitzgibbon. Bundle adjustment—a mod- ern synthesis. In Vision Algorithms: Theory and Prac- tice: International Workshop on Vision Algorithms Corfu, Greece, September 21–22, 1999 Proceedings , pages 298–

    1999

  55. [63]

    3d reconstruction with spatial memory

    Hengyi Wang and Lourdes Agapito. 3d reconstruction with spatial memory. arXiv preprint arXiv:2408.16061, 2024. 1, 2, 3, 5, 6, 7, 8

    2024 arXiv

  56. [64]

    Co- slam: Joint coordinate and sparse parametric encodings for neural real-time slam

    Hengyi Wang, Jingwen Wang, and Lourdes Agapito. Co- slam: Joint coordinate and sparse parametric encodings for neural real-time slam. In Proceedings of the IEEE/CVF Con- ference on Computer Vision and Pattern Recognition, pages 13293–13302, 2023. 7

    2023

  57. [65]

    Vggsfm: Visual geometry grounded deep structure from motion

    Jianyuan Wang, Nikita Karaev, Christian Rupprecht, and David Novotny. Vggsfm: Visual geometry grounded deep structure from motion. In Proceedings of the IEEE/CVF con- ference on computer vision and pattern recognition , pages 21686–21697, 2024. 2

    2024

  58. [66]

    Neus: Learning neural implicit surfaces by volume rendering for multi-view reconstruction

    Peng Wang, Lingjie Liu, Yuan Liu, Christian Theobalt, Taku Komura, and Wenping Wang. Neus: Learning neural implicit surfaces by volume rendering for multi-view reconstruction. arXiv preprint arXiv:2106.10689, 2021. 2

    2021 arXiv

  59. [67]

    Continuous 3d perception model with persistent state

    Qianqian Wang, Yifei Zhang, Aleksander Holynski, Alexei A Efros, and Angjoo Kanazawa. Continuous 3d perception model with persistent state. arXiv preprint arXiv:2501.12387, 2025. 2, 5, 6, 7

    2025 arXiv

  60. [68]

    Dust3r: Geometric 3d vi- sion made easy

    Shuzhe Wang, Vincent Leroy, Yohann Cabon, Boris Chidlovskii, and Jerome Revaud. Dust3r: Geometric 3d vi- sion made easy. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 20697– 20709, 2024. 1, 2, 3, 5, 6, 7

    2024

  61. [69]

    CroCo v2: Improved Cross-view Completion Pre- training for Stereo Matching and Optical Flow

    Philippe Weinzaepfel, Thomas Lucas, Vincent Leroy, Yohann Cabon, Vaibhav Arora, Romain Br ´egier, Gabriela Csurka, Leonid Antsfeld, Boris Chidlovskii, and J´erˆome Re- vaud. CroCo v2: Improved Cross-view Completion Pre- training for Stereo Matching and Optical Flow. In Proc. I...

    2023

  62. [70]

    CroCo: Self- Supervised Pre-training for 3D Vision Tasks by Cross-View Completion

    Weinzaepfel, Philippe and Leroy, Vincent and Lucas, Thomas and Br ´egier, Romain and Cabon, Yohann and Arora, Vaibhav and Antsfeld, Leonid and Chidlovskii, Boris and Csurka, Gabriela and Revaud J ´erˆome. CroCo: Self- Supervised Pre-training for 3D Vision Tasks by Cross-View C...

    2022

  63. [71]

    Robust global translations with 1dsfm

    Kyle Wilson and Noah Snavely. Robust global translations with 1dsfm. In European conference on computer vision , pages 61–75. Springer, 2014. 1

    2014

  64. [72]

    Towards linear-time incremental struc- ture from motion

    Changchang Wu. Towards linear-time incremental struc- ture from motion. In 2013 International Conference on 3D Vision-3DV 2013, pages 127–134. IEEE, 2013. 1

    2013

  65. [73]

    Multicore bundle adjustment

    Changchang Wu, Sameer Agarwal, Brian Curless, and Steven M Seitz. Multicore bundle adjustment. In CVPR 2011, pages 3057–3064. IEEE, 2011. 1

    2011

  66. [74]

    Frozenrecon: Pose-free 3d scene reconstruction with frozen depth models

    Guangkai Xu, Wei Yin, Hao Chen, Chunhua Shen, Kai Cheng, and Feng Zhao. Frozenrecon: Pose-free 3d scene reconstruction with frozen depth models. In Proceedings of the IEEE/CVF International Conference on Computer Vi- sion, pages 9310–9320, 2023. 5

    2023

  67. [75]

    Liang, Mikael Henaff, Hao Tang, Ang Cao, Joyce Chai, Franziska Meier, and Matt 11 Feiszli

    Jianing Yang, Alexander Sax, Kevin J. Liang, Mikael Henaff, Hao Tang, Ang Cao, Joyce Chai, Franziska Meier, and Matt 11 Feiszli. Fast3r: Towards 3d reconstruction of 1000+ im- ages in one forward pass. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Re...

    2025

  68. [76]

    Depth anything: Unleashing the power of large-scale unlabeled data

    Lihe Yang, Bingyi Kang, Zilong Huang, Xiaogang Xu, Jiashi Feng, and Hengshuang Zhao. Depth anything: Unleashing the power of large-scale unlabeled data. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10371–10381, 2024. 2

    2024

  69. [77]

    D3vo: Deep depth, deep pose and deep uncer- tainty for monocular visual odometry

    Nan Yang, Lukas von Stumberg, Rui Wang, and Daniel Cremers. D3vo: Deep depth, deep pose and deep uncer- tainty for monocular visual odometry. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 1281–1292, 2020. 2

    2020

  70. [78]

    Spectrally pruned gaussian fields with neural compensation,

    Runyi Yang, Zhenxin Zhu, Zhou Jiang, Baijun Ye, Xiaoxue Chen, Yifei Zhang, Yuantao Chen, Jian Zhao, and Hao Zhao. Spectrally pruned gaussian fields with neural compensation,

  71. [79]

    Mvsnet: Depth inference for unstructured multi-view stereo

    Yao Yao, Zixin Luo, Shiwei Li, Tian Fang, and Long Quan. Mvsnet: Depth inference for unstructured multi-view stereo. In Proceedings of the European conference on computer vi- sion (ECCV), pages 767–783, 2018. 2

    2018

  72. [80]

    Blendedmvs: A large- scale dataset for generalized multi-view stereo networks

    Yao Yao, Zixin Luo, Shiwei Li, Jingyang Zhang, Yufan Ren, Lei Zhou, Tian Fang, and Long Quan. Blendedmvs: A large- scale dataset for generalized multi-view stereo networks. In Proceedings of the IEEE/CVF conference on computer vi- sion and pattern recognition, pages 1790–1799, 2020. 6

    2020

  73. [81]

    Blending distributed nerfs with tri-stage robust pose opti- mization

    Baijun Ye, Caiyun Liu, Xiaoyu Ye, Yuantao Chen, Yuhai Wang, Zike Yan, Yongliang Shi, Hao Zhao, and Guyue Zhou. Blending distributed nerfs with tri-stage robust pose opti- mization. In 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , pages 7975–...

    2024

  74. [82]

    Scannet++: A high-fidelity dataset of 3d in- door scenes

    Chandan Yeshwanth, Yueh-Cheng Liu, Matthias Nießner, and Angela Dai. Scannet++: A high-fidelity dataset of 3d in- door scenes. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 12–22, 2023. 6

    2023

  75. [83]

    Mip-splatting: Alias-free 3d gaussian splat- ting

    Zehao Yu, Anpei Chen, Binbin Huang, Torsten Sattler, and Andreas Geiger. Mip-splatting: Alias-free 3d gaussian splat- ting. In Proceedings of the IEEE/CVF conference on com- puter vision and pattern recognition , pages 19447–19456,

  76. [84]

    Monst3r: A simple approach for estimat- ing geometry in the presence of motion

    Junyi Zhang, Charles Herrmann, Junhwa Hur, Varun Jam- pani, Trevor Darrell, Forrester Cole, Deqing Sun, and Ming- Hsuan Yang. Monst3r: A simple approach for estimat- ing geometry in the presence of motion. arXiv preprint arxiv:2410.03825, 2024. 2, 7

    arXiv

  77. [85]

    Drone-assisted road gaussian splatting with cross- view uncertainty

    Saining Zhang, Baijun Ye, Xiaoxue Chen, Yuantao Chen, Zongzheng Zhang, Cheng Peng, Yongliang Shi, and Hao Zhao. Drone-assisted road gaussian splatting with cross- view uncertainty. arXiv preprint arXiv:2408.15242 , 2024. 2

    2024 arXiv

  78. [86]

    Flare: Feed-forward geometry, appearance and camera estimation from uncalibrated sparse views.arXiv preprint arXiv:2502.12138, 2025

    Shangzhan Zhang, Jianyuan Wang, Yinghao Xu, Nan Xue, Christian Rupprecht, Xiaowei Zhou, Yujun Shen, and Gor- don Wetzstein. Flare: Feed-forward geometry, appearance and camera estimation from uncalibrated sparse views.arXiv preprint arXiv:2502.12138, 2025. 2

    2025

  79. [87]

    Particlesfm: Exploiting dense point trajecto- ries for localizing moving cameras in the wild

    Wang Zhao, Shaohui Liu, Hengkai Guo, Wenping Wang, and Yong-Jin Liu. Particlesfm: Exploiting dense point trajecto- ries for localizing moving cameras in the wild. In European Conference on Computer Vision , pages 523–542. Springer,

  80. [88]

    Nice-slam: Neural implicit scalable encoding for slam

    Zihan Zhu, Songyou Peng, Viktor Larsson, Weiwei Xu, Hu- jun Bao, Zhaopeng Cui, Martin R Oswald, and Marc Polle- feys. Nice-slam: Neural implicit scalable encoding for slam. In Proceedings of the IEEE/CVF conference on computer vi- sion and pattern recognition, pages 12786–1279...

    2022

  81. [89]

    Nicer-slam: Neural implicit scene encoding for rgb slam

    Zihan Zhu, Songyou Peng, Viktor Larsson, Zhaopeng Cui, Martin R Oswald, Andreas Geiger, and Marc Pollefeys. Nicer-slam: Neural implicit scene encoding for rgb slam. In 2024 International Conference on 3D Vision (3DV) , pages 42–52. IEEE, 2024. 2 12

    2024

Pith tools

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