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REVIEW 3 major objections 5 minor 2 cited by

E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models

T0 review · 3 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read This paper introduces E3D-Bench, the first systematic benchmark for end-to-end 3D geometric foundation models, covering 16 models, five geometry tasks, and inference efficiency.

desk verdict A genuinely useful benchmark protocol with a serious data-integrity problem in Table 2 that must be fixed before the results can be trusted. read the letter →

arxiv 2506.01933 v3 pith:LFTWL7OJ submitted 2025-06-02 cs.CV

Wenyan Cong , Yiqing Liang , Yancheng Zhang , Ziyi Yang , Yan Wang , Boris Ivanovic , Marco Pavone , Chen Chen
show 2 more authors
Zhangyang Wang Zhiwen Fan
This is my paper
classification cs.CV
keywords 3Dgeometricfoundationmodelsbenchmarkmulti-viewreconstructiondepthestimationcameraposenovelviewsynthesisinferenceefficiencyend-to-endperception
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

E3D-Bench is a benchmark built to answer whether end-to-end 3D geometric foundation models—networks that predict point clouds, depth, and camera poses directly from images—can serve as reliable, efficient building blocks for spatial intelligence. The paper evaluates 16 such models on five geometry tasks (sparse-view depth, video depth, multi-view reconstruction, relative pose, and novel view synthesis) plus inference efficiency, across indoor, outdoor, drone, dynamic, and air-ground datasets. Its central claim is that current models excel on simple sub-tasks and in-domain data but degrade with task complexity, extreme distribution shifts, and metric-scale demands, and that no single architecture family wins across the board. If right, the benchmark becomes the shared reference for comparing GFMs and guides where scaling and training data should go next.

What carries the argument

The carrying object is the benchmark itself: a standardized evaluation toolkit that automates dataset handling, evaluation protocols, and metric computation, applied uniformly to 16 GFMs. The comparison is made fair across heterogeneous models by extracting depth from the $z$-coordinate of predicted point maps, using ground-truth intrinsics and poses for depth and reconstruction tasks, applying per-view median alignment for normalized models and sequence-level scale-and-shift alignment for video depth, aligning trajectories with $\mathrm{Sim}(3)$ least-squares alignment, selecting quasi-optimal source views following RobustMVD, and using test-time photometric pose optimization for novel-view synthesis.

What would settle it

Re-run the leaderboards under a no-frills protocol—predicted (not ground-truth) poses and intrinsics, no median alignment, a single fixed random source-view schedule—and check whether VGGT, CUT3R, and the top diffusion models keep their relative order; if the order flips, the evaluation protocol, not model capability, drove the benchmark's findings.

Watch

Extended reading notes

Core claim

On the paper's own terms, the discovery is that systematic comparison changes the field's self-assessment: GFMs are strong enough to beat task-specific specialists on depth and pose, yet none is a plug-and-play solution. Pair-view geometry outperforms true multi-view inference; single-attribute predictions (depth or pose) are more reliable than full scene reconstruction; relative, normalized outputs are far more accurate than metric-scale ones; generalization holds for aerial and driving shifts but collapses on air-ground pairs; feed-forward ViTs and diffusion models each win some tasks; models built on strong 2D features, exemplified by VGGT's DINO-initialized backbone, lead the pack; and none of the models reaches real-time latency, with even efficient ones taking tens of seconds for hundreds of views.

Load-bearing premise

The rankings assume the benchmark's alignment and pose-handling choices treat normalized and metric-scale, feed-forward and diffusion, and pair-view and multi-view models even-handedly; if those choices favor one family, the headline conclusions would be artifacts of the protocol.

Editorial extensions

If this is right

  • New GFM papers can be placed against a single reported table instead of per-task, per-dataset numbers, making capability claims easier to check.
  • Because joint prediction of pose, depth, and matching correlates with stronger generalization, decomposing hard tasks into sub-problems is a promising path for improving learning under limited 3D data.
  • The benchmark's generalization failures point to missing aerial-ground and metric-depth supervision, so training on more diverse, metric-scale-annotated data is the concrete lever for robustness.
  • Efficiency is a first-class axis: online-registration models such as Spann3R and CUT3R balance accuracy against latency, while global-alignment models risk out-of-memory failures as views grow.
  • Backbone choice should be task-driven: no family dominates, and upgrading the 2D feature extractor is a concrete lever for 3D gains.

Reading between the lines

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

  • Editorial extension: the headline rankings are only as robust as the alignment protocol; re-running with a no-alignment metric-scale protocol, or with predicted rather than ground-truth poses, could reshuffle the order of VGGT, CUT3R, and the diffusion models.
  • Editorial extension: isolating geometry with ground-truth poses means real-world performance is likely worse across the board, so a joint pose-and-geometry leaderboard would better predict deployment behavior.
  • Editorial extension: the DINO-backbone correlation suggests a controlled ablation—keep the GFM architecture and training data fixed, swap the 2D backbone, and measure the shift in 3D metrics.
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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 introduces E3D-Bench, a benchmark for end-to-end 3D geometric foundation models (GFMs). It evaluates 16 models across five tasks (sparse-view depth estimation, video depth estimation, multi-view relative pose estimation, multi-view 3D reconstruction, and novel view synthesis), plus an inference-efficiency analysis, using both standard and out-of-distribution datasets. The authors propose standardized protocols for dataset handling, alignment, and metric computation, and derive findings on task difficulty, domain generalization, architecture choice, and efficiency. They claim this is the first systematic benchmark spanning these tasks and scenarios.

Significance. If the results are reliable, E3D-Bench would fill a real gap: the 3D GFM literature has grown rapidly but lacks a common evaluation platform. The paper's strengths include the breadth of tasks and datasets, the inclusion of both metric-scale and normalized models, explicit efficiency benchmarking, and transparent disclosure of protocol choices in the appendices. The reported rankings do not favor the authors' own LSM model, which suggests that the evaluation is not cherry-picked. However, the data-integrity issue in Table 2, the absence of error bars, and the lack of sensitivity analysis currently prevent the benchmark from serving as a trustworthy reference; these issues are fixable but require substantial additional work.

major comments (3)
  1. [Table 2] In the metric-scale block of Table 2, the rows for MASt3R and CUT3R are identical on every dataset (e.g., DTU 84.904/0.000, ScanNet 93.584/0.000, KITTI 99.069/0.000, ETH3D 97.021 vs. 97.022, and Tanks and Temples 98.234/0.000). Since the normalized-scale rows for these two models differ substantially (e.g., DTU AbsRel 3.343 vs. 6.200 and KITTI 9.542 vs. 23.849), the identical metric failures are not plausible as genuine model behavior and appear to be a transcription or scoring bug. The finding in Section 4.1 that 'metric-scale depth estimation remains a key challenge' rests heavily on these rows, so the authors must re-run the evaluation and correct the table or provide a valid explanation. The absence of error bars makes it impossible to know whether other close rankings are similarly affected.
  2. [Tables 2-5 and Figure 2] The paper reports no error bars, confidence intervals, or multiple-run statistics for any of the quantitative comparisons. Comparative claims such as 'VGGT consistently achieves the best performance' (Section 3.2) and the architecture findings in Section 4.3 rely on small numeric differences (e.g., Video Depth on Bonn: CUT3R 0.068 vs. VGGT 0.056; Align3R 0.062 vs. MonST3R 0.061). Without variance estimates, the reader cannot assess whether these differences are meaningful. The benchmark should report per-scene standard errors or per-run statistics, and ideally a statistical test for the headline comparisons, especially because the paper's goal is to be the reference for comparing GFMs.
  3. [Appendices B.1, B.2, C.2 and Section 3.3] The evaluation protocol makes several consequential choices: per-view median alignment for normalized sparse-view depth, sequence-level scale-and-shift alignment for video depth, Sim(3) Umeyama alignment for pose trajectories, quasi-optimal source-view selection from RobustMVD, and test-time pose optimization for novel view synthesis. These choices are disclosed, but the paper does not test whether rankings are robust to them. Since the central claim is 'fair, reproducible comparisons,' the authors should provide a sensitivity analysis for at least the most consequential choices: per-frame vs. per-sequence alignment, nearest-neighbor vs. quasi-optimal source views, and evaluation with vs. without ground-truth intrinsics and poses. Without such evidence, findings like 'no single backbone type dominates' could be an artifact of the specific protocol rather than of model capability.
minor comments (5)
  1. [Section 4.1] The sentence 'MonST3R ranks among the top-3 in video depth estimation (Tab.3) but performs poorly in 3D reconstruction (Tab.2)' should reference Table 5 for 3D reconstruction; Table 2 reports sparse-view depth estimation, not 3D reconstruction.
  2. [Table 4] The header 'KITTI Ordometry' should be 'KITTI Odometry'.
  3. [Abstract and Section 5] The abstract and Section 1 state 'five core tasks,' while Section 5 states 'six core tasks'; the count should be reconciled.
  4. [Header/links] The website and code URLs in the header appear garbled (e.g., 'g♀be' and '/gtb'); please provide clean, correct links.
  5. [Code release] The paper promises that 'all code, evaluation scripts, and processed data will be publicly released' but the release is not yet available; to substantiate the reproducibility claim, the toolkit should be made available at least to reviewers before final acceptance.

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity: empirical benchmark rankings are not derived from their inputs; only a minor non-load-bearing self-citation appears.

full rationale

The paper is a benchmark and empirical evaluation, not a formal derivation. Its conclusions—such as VGGT leading feed-forward models, metric-scale depth remaining hard, and no single backbone dominating—are read directly from Tables 2–5 and Figure 2, and none of those tables are produced by solving an equation in which the conclusion was an input. The evaluation protocols in Appendix B use standard, uniformly applied alignments (per-view median scaling, sequence-level scale-and-shift, Sim(3) Umeyama, quasi-optimal source views); these choices can affect rankings but are not defined in terms of the paper's findings. There is one minor overlapping-author citation: [68] is cited in Appendix A.1 when introducing the term 'GFMs,' and other works by the authors are cited as model references, but none of these citations supplies the benchmark's conclusions, so the self-citation is not load-bearing. The LSM co-authorship does not create circularity: the paper explicitly groups LSM with DUSt3R because 'LSM builds on top of DUSt3R without modifying its weights,' and the reported results do not favor LSM—LSM underperforms in novel view synthesis and is not the top method on most tasks. A data-integrity concern is the near-identical metric-scale rows for MASt3R and CUT3R in Table 2 (84.904/0.000 through 98.234/0.000); this may indicate a transcription or protocol bug and is a correctness risk, but it is not circular because the numbers are not constructed from each other or from a fitted parameter. The paper's stated limitation (GPU-only evaluation) is also not a circular step. Overall, no prediction is equivalent to its input by construction; the score of 1 reflects only the minor overlapping-author citations and otherwise independent empirical content.

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

No free parameters or invented entities. The benchmark is an empirical evaluation; its only inputs are protocol assumptions about fairness and representativeness, which are listed as axioms.

assumptions (4)
  • domain assumption The selected tasks and datasets are representative of real-world 3D perception scenarios.
    The benchmark's conclusions about strengths and weaknesses generalize only if the chosen datasets and evaluation protocols are representative; the paper assumes this without external validation.
  • domain assumption Metric definitions and alignment procedures are fair across models.
    Comparisons use per-view median alignment for normalized depth, sequence-level scale-and-shift alignment for video depth, and Sim(3) Umeyama alignment for poses; rankings can shift if these choices favor certain model families.
  • domain assumption Ground-truth poses, depths, and masks from the datasets are accurate and consistent.
    All metrics are computed against these labels, so label noise or inconsistent preprocessing would propagate into every table.
  • domain assumption Sim(3) alignment is an appropriate way to compare predicted and ground-truth trajectories.
    Pose metrics after Umeyama alignment remove metric scale, and the paper uses this to compare models with unknown scale; this is standard, but it is still an assumption about what the metric captures.

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Cite this review

Pith. "Pith review of E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models." pith.science (2026). https://pith.science/paper/LFTWL7OJ

@misc{pith2026250601933,
  author       = {Pith},
  title        = {Pith review of: E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/LFTWL7OJ}},
  note         = {Machine review of arXiv:2506.01933}
}
read the original abstract

Spatial intelligence, encompassing 3D reconstruction, perception, and reasoning, is fundamental to applications such as robotics, aerial imaging, and extended reality. A key enabler is the real-time, accurate estimation of core 3D attributes (camera parameters, point clouds, depth maps, and 3D point tracks) from unstructured or streaming imagery. Inspired by the success of large foundation models in language and 2D vision, a new class of end-to-end 3D geometric foundation models (GFMs) has emerged, directly predicting dense 3D representations in a single feed-forward pass, eliminating the need for slow or unavailable precomputed camera parameters. Since late 2023, the field has exploded with diverse variants, but systematic evaluation is lacking. In this work, we present the first comprehensive benchmark for 3D GFMs, covering five core tasks: sparse-view depth estimation, video depth estimation, 3D reconstruction, multi-view pose estimation, novel view synthesis, and spanning both standard and challenging out-of-distribution datasets. Our standardized toolkit automates dataset handling, evaluation protocols, and metric computation to ensure fair, reproducible comparisons. We evaluate 16 state-of-the-art GFMs, revealing their strengths and limitations across tasks and domains, and derive key insights to guide future model scaling and optimization. All code, evaluation scripts, and processed data will be publicly released to accelerate research in 3D spatial intelligence.

Figures

Figures reproduced from arXiv: 2506.01933 by the authors.

Figure 1
Figure 1. E3D-Bench evaluates 16 recent 3D Geometric Foundation Models (GFMs), spanning two major architectural families: feed-forward ViT-based and diffusion-based models, on both effectiveness and efficiency. For clarity, we visualize only the top methods per task. All metrics in bin chart except inference time, are averaged per scene, normalized, and converted to a "higher-is-better" scale for consistent comparison. Abstra… view at source ↗
Figure 2
Figure 2. Inference Time (s) (Left) and Peak GPU Memory Usage (GB) (Right) on different numbers [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗

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Forward citations

Cited by 2 Pith papers

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  2. Seeing Where to Deploy: Metric RGB-Based Traversability Analysis for Aerial-to-Ground Hidden Space Inspection

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Reference graph

Works this paper leans on

137 extracted references · 25 canonical work pages · cited by 2 Pith papers

  1. [1]

    Pixel- wise view selection for unstructured multi-view stereo

    Johannes Lutz Schönberger, Enliang Zheng, Marc Pollefeys, and Jan-Michael Frahm. Pixel- wise view selection for unstructured multi-view stereo. In European Conference on Computer Vision (ECCV), 2016

    2016

  2. [2]

    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 vision (ECCV), pages 767–783, 2018

    2018

  3. [3]

    Cascade cost volume for high-resolution multi-view stereo and stereo matching

    Xiaodong Gu, Zhiwen Fan, Siyu Zhu, Zuozhuo Dai, Feitong Tan, and Ping Tan. Cascade cost volume for high-resolution multi-view stereo and stereo matching. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 2495–2504, 2020

    2020

  4. [4]

    Dense visual slam for rgb-d cameras

    Christian Kerl, Jürgen Sturm, and Daniel Cremers. Dense visual slam for rgb-d cameras. In 2013 IEEE/RSJ international conference on intelligent robots and systems, pages 2100–2106. IEEE, 2013

    2013

  5. [5]

    Elasticfusion: Real-time dense slam and light source estimation

    Thomas Whelan, Renato F Salas-Moreno, Ben Glocker, Andrew J Davison, and Stefan Leutenegger. Elasticfusion: Real-time dense slam and light source estimation. The Interna- tional Journal of Robotics Research, 35(14):1697–1716, 2016

    2016

  6. [6]

    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 Conference on Computer Vision and Pattern Recognition (CVPR), July 2017

    2017

  7. [7]

    Consistent video depth estimation

    Xuan Luo, Jia-Bin Huang, Richard Szeliski, Kevin Matzen, and Johannes Kopf. Consistent video depth estimation. 39(4), 2020

    2020

  8. [8]

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

    Katrin Lasinger, René Ranftl, Konrad Schindler, and Vladlen Koltun. Towards robust monoc- ular depth estimation: Mixing datasets for zero-shot cross-dataset transfer. arXiv preprint arXiv:1907.01341, 2019

    arXiv 1907

Show all 137 references
  1. [9]

    Gpt-4 technical report

    Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774, 2023

    2023 arXiv

  2. [10]

    Segment anything

    Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C Berg, Wan-Yen Lo, et al. Segment anything. In Proceedings of the IEEE/CVF international conference on computer vision, pages 4015–4026, 2023

    2023

  3. [11]

    Byte latent transformer: Patches scale better than tokens

    Artidoro Pagnoni, Ram Pasunuru, Pedro Rodriguez, John Nguyen, Benjamin Muller, Margaret Li, Chunting Zhou, Lili Yu, Jason Weston, Luke Zettlemoyer, et al. Byte latent transformer: Patches scale better than tokens. arXiv preprint arXiv:2412.09871, 2024

    2024 arXiv

  4. [12]

    Videolifter: Lifting videos to 3d with fast hierarchical stereo alignment

    Wenyan Cong, Hanqing Zhu, Kevin Wang, Jiahui Lei, Colton Stearns, Yuanhao Cai, Dilin Wang, Rakesh Ranjan, Matt Feiszli, Leonidas Guibas, et al. Videolifter: Lifting videos to 3d with fast hierarchical stereo alignment. arXiv preprint arXiv:2501.01949, 2025

    2025 arXiv

  5. [13]

    Dust3r: Geometric 3d vision made easy

    Shuzhe Wang, Vincent Leroy, Yohann Cabon, Boris Chidlovskii, and Jerome Revaud. Dust3r: Geometric 3d vision made easy. In CVPR, 2024

    2024

  6. [14]

    Grounding image matching in 3d with mast3r

    Vincent Leroy, Yohann Cabon, and Jerome Revaud. Grounding image matching in 3d with mast3r. In ECCV, 2024

    2024

  7. [15]

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

    Jianing Yang, Alexander Sax, Kevin J. Liang, Mikael Henaff, Hao Tang, Ang Cao, Joyce Chai, Franziska Meier, and Matt Feiszli. Fast3r: Towards 3d reconstruction of 1000+ images in one forward pass. In CVPR, 2025. 10

    2025

  8. [16]

    MonST3r: A simple approach for estimating geometry in the presence of motion

    Junyi Zhang, Charles Herrmann, Junhwa Hur, Varun Jampani, Trevor Darrell, Forrester Cole, Deqing Sun, and Ming-Hsuan Yang. MonST3r: A simple approach for estimating geometry in the presence of motion. In ICLR, 2025

    2025

  9. [17]

    Vggt: Visual geometry grounded transformer

    Jianyuan Wang, Minghao Chen, Nikita Karaev, Andrea Vedaldi, Christian Rupprecht, and David Novotny. Vggt: Visual geometry grounded transformer. In CVPR, 2025

    2025

  10. [18]

    Large spatial model: End-to-end unposed images to semantic 3d

    Zhiwen Fan, Jian Zhang, Wenyan Cong, Peihao Wang, Renjie Li, Kairun Wen, Shijie Zhou, Achuta Kadambi, Zhangyang Wang, Danfei Xu, Boris Ivanovic, Marco Pavone, and Yue Wang. Large spatial model: End-to-end unposed images to semantic 3d. In NeurIPS, 2024

    2024

  11. [19]

    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. In CVPR, 2025

    2025

  12. [20]

    Aether: Geometric-aware unified world modeling

    Aether Team, Haoyi Zhu, Yifan Wang, Jianjun Zhou, Wenzheng Chang, Yang Zhou, Zizun Li, Junyi Chen, Chunhua Shen, Jiangmiao Pang, et al. Aether: Geometric-aware unified world modeling. arXiv preprint arXiv:2503.18945, 2025

    2025 arXiv

  13. [21]

    Geo4d: Leverag- ing video generators for geometric 4d scene reconstruction, 2025

    Zeren Jiang, Chuanxia Zheng, Iro Laina, Diane Larlus, and Andrea Vedaldi. Geo4d: Leverag- ing video generators for geometric 4d scene reconstruction, 2025

    2025

  14. [22]

    No pose, no problem: Surprisingly simple 3d gaussian splats from sparse unposed images

    Botao Ye, Sifei Liu, Haofei Xu, Xueting Li, Marc Pollefeys, Ming-Hsuan Yang, and Songyou Peng. No pose, no problem: Surprisingly simple 3d gaussian splats from sparse unposed images. arXiv preprint arXiv:2410.24207, 2024

    2024 arXiv

  15. [23]

    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. In CVPR, 2025

    2025

  16. [24]

    Splatt3r: Zero-shot gaussian splatting from uncalibrated image pairs, 2024

    Brandon Smart, Chuanxia Zheng, Iro Laina, and Victor Adrian Prisacariu. Splatt3r: Zero-shot gaussian splatting from uncalibrated image pairs, 2024

    2024

  17. [25]

    Easi3r: Estimating disentangled motion from dust3r without training

    Xingyu Chen, Yue Chen, Yuliang Xiu, Andreas Geiger, and Anpei Chen. Easi3r: Estimating disentangled motion from dust3r without training. arXiv preprint arXiv:2503.24391, 2025

    2025

  18. [26]

    3d reconstruction with spatial memory

    Hengyi Wang and Lourdes Agapito. 3d reconstruction with spatial memory. In 3DV, 2025

    2025

  19. [27]

    Geometrycrafter: Consistent geometry estimation for open-world videos with diffusion priors

    Tian-Xing Xu, Xiangjun Gao, Wenbo Hu, Xiaoyu Li, Song-Hai Zhang, and Ying Shan. Geometrycrafter: Consistent geometry estimation for open-world videos with diffusion priors. arXiv preprint arXiv:2504.01016, 2025

    2025 arXiv

  20. [28]

    Flare: Feed-forward geometry, appearance and camera estimation from uncalibrated sparse views

    Shangzhan Zhang, Jianyuan Wang, Yinghao Xu, Nan Xue, Christian Rupprecht, Xiaowei Zhou, Yujun Shen, and Gordon Wetzstein. Flare: Feed-forward geometry, appearance and camera estimation from uncalibrated sparse views. In CVPR, 2025

    2025

  21. [29]

    Emerging properties in self-supervised vision transformers

    Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin. Emerging properties in self-supervised vision transformers. InProceedings of the International Conference on Computer Vision (ICCV), 2021

    2021

  22. [30]

    Structure-from-motion revisited

    Johannes Lutz Schönberger and Jan-Michael Frahm. Structure-from-motion revisited. In Conference on Computer Vision and Pattern Recognition (CVPR), 2016

    2016

  23. [31]

    Large scale multi-view stereopsis evaluation

    Rasmus Jensen et al. Large scale multi-view stereopsis evaluation. In CVPR, 2014

    2014

  24. [32]

    A multi-view stereo benchmark with high-resolution images and multi-camera videos

    Thomas Schops, Johannes L Schonberger, Silvano Galliani, Torsten Sattler, Konrad Schindler, Marc Pollefeys, and Andreas Geiger. A multi-view stereo benchmark with high-resolution images and multi-camera videos. In Proceedings of the IEEE conference on computer vision and patte...

    2017

  25. [33]

    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. In CVPR, 2012

    2012

  26. [34]

    Tanks and temples: Bench- marking large-scale scene reconstruction

    Arno Knapitsch, Jaesik Park, Qian-Yi Zhou, and Vladlen Koltun. Tanks and temples: Bench- marking large-scale scene reconstruction. ACM Transactions on Graphics (ToG), 36(4):1–13, 2017. 11

    2017

  27. [35]

    Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, and Matthias Nießner

    Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, and Matthias Nießner. Scannet: Richly-annotated 3d reconstructions of indoor scenes. In CVPR, 2017

    2017

  28. [36]

    Sparsity invariant cnns

    Jonas Uhrig, Nick Schneider, Lukas Schneider, Uwe Franke, Thomas Brox, and Andreas Geiger. Sparsity invariant cnns. In 2017 international conference on 3D Vision (3DV), pages 11–20. IEEE, 2017

    2017

  29. [37]

    Depth map prediction from a single image using a multi-scale deep network

    David Eigen, Christian Puhrsch, and Rob Fergus. Depth map prediction from a single image using a multi-scale deep network. Advances in neural information processing systems, 27, 2014

    2014

  30. [38]

    A benchmark for multi-view stereo depth estimation under view-point and lighting variations

    Max Schroppel, Mathias Rothermel, and et al. A benchmark for multi-view stereo depth estimation under view-point and lighting variations. In ECCV, 2022

    2022

  31. [39]

    A benchmark for visual-inertial odometry in the presence of motion blur

    Emanuele Palazzolo and Stefan Leutenegger. A benchmark for visual-inertial odometry in the presence of motion blur. In ICRA, 2019

    2019

  32. [40]

    A benchmark for the evaluation of rgb-d slam systems

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

    2012

  33. [41]

    A naturalistic open source movie for optical flow evaluation

    DJ Butler, J Wulff, GB Stanley, and MJ Black. A naturalistic open source movie for optical flow evaluation. ECCV, 2012

    2012

  34. [42]

    Pointodyssey: A large-scale benchmark for robust video depth estimation

    Boyang Zhao, Renjie Liao, Shiry Yin, et al. Pointodyssey: A large-scale benchmark for robust video depth estimation. arXiv preprint arXiv:2402.12345, 2024

    2024 arXiv

  35. [43]

    Syndrone-multi- modal uav dataset for urban scenarios

    Giulia Rizzoli, Francesco Barbato, Matteo Caligiuri, and Pietro Zanuttigh. Syndrone-multi- modal uav dataset for urban scenarios. In Proceedings of the IEEE/CVF International Confer- ence on Computer Vision, pages 2210–2220, 2023

    2023

  36. [44]

    Video depth anything: Consistent depth estimation for super-long videos

    Sili Chen, Hengkai Guo, Shengnan Zhu, Feihu Zhang, Zilong Huang, Jiashi Feng, and Bingyi Kang. Video depth anything: Consistent depth estimation for super-long videos. arXiv:2501.12375, 2025

    2025 arXiv

  37. [45]

    Depth any video with scalable synthetic data

    Honghui Yang, Di Huang, Wei Yin, Chunhua Shen, Haifeng Liu, Xiaofei He, Binbin Lin, Wanli Ouyang, and Tong He. Depth any video with scalable synthetic data. arXiv preprint arXiv:2410.10815, 2024

    2024 arXiv

  38. [46]

    Marigold: Affordable adaptation of diffusion-based image generators for image analysis, 2025

    Bingxin Ke, Kevin Qu, Tianfu Wang, Nando Metzger, Shengyu Huang, Bo Li, Anton Obukhov, and Konrad Schindler. Marigold: Affordable adaptation of diffusion-based image generators for image analysis, 2025

    2025

  39. [47]

    Depthcrafter: Generating consistent long depth sequences for open-world videos

    Wenbo Hu, Xiangjun Gao, Xiaoyu Li, Sijie Zhao, Xiaodong Cun, Yong Zhang, Long Quan, and Ying Shan. Depthcrafter: Generating consistent long depth sequences for open-world videos. arXiv preprint arXiv:2409.02095, 2024

    2024 arXiv

  40. [48]

    Learning object-centric representations of multi-object scenes from multiple views

    Nancy Ruiz, Rohit Varma, Justin Johnson, and Angjoo Kanazawa. Learning object-centric representations of multi-object scenes from multiple views. In CVPR, 2022

    2022

  41. [49]

    Tinghui Zhou, Matthew Brown, Noah Snavely, and David G. Lowe. Unsupervised learning of depth and ego-motion from video. In CVPR, 2017

    2017

  42. [50]

    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. In Conference on Computer Vision and Pattern Recognition (CVPR), 2012

    2012

  43. [51]

    Tapvid-3d: A benchmark for tracking any point in 3d

    Skanda Koppula, Ignacio Rocco, Yi Yang, Joe Heyward, João Carreira, Andrew Zisserman, Gabriel Brostow, and Carl Doersch. Tapvid-3d: A benchmark for tracking any point in 3d. arXiv preprint arXiv:2407.05921, 2024

    2024 arXiv

  44. [52]

    Acid: Aerial-captured image dataset for visual localization

    Liyang Zhou et al. Acid: Aerial-captured image dataset for visual localization. In ECCV, 2022. 12

    2022

  45. [53]

    Ultrra challenge 2025, 2024

    Neil Joshi, Joshua Carney, Nathanael Kuo, Homer Li, Cheng Peng, and Myron Brown. Ultrra challenge 2025, 2024

    2025

  46. [54]

    Least-squares estimation of transformation parameters between two point patterns

    Shinji Umeyama. Least-squares estimation of transformation parameters between two point patterns. IEEE Transactions on Pattern Analysis & Machine Intelligence, 13(04):376–380, 1991

    1991

  47. [55]

    Aerialmegadepth: Learning aerial-ground reconstruction and view synthesis

    Khiem Vuong, Anurag Ghosh, Deva Ramanan, Srinivasa Narasimhan, and Shubham Tulsiani. Aerialmegadepth: Learning aerial-ground reconstruction and view synthesis. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2025

    2025

  48. [56]

    Instantsplat: Sparse-view sfm-free gaussian splatting in seconds, 2024

    Zhiwen Fan, Wenyan Cong, Kairun Wen, Kevin Wang, Jian Zhang, Xinghao Ding, Danfei Xu, Boris Ivanovic, Marco Pavone, Georgios Pavlakos, Zhangyang Wang, and Yue Wang. Instantsplat: Sparse-view sfm-free gaussian splatting in seconds, 2024

    2024

  49. [57]

    Scene coordinate regression forests for camera relocalization in rgb-d images

    Jamie Shotton et al. Scene coordinate regression forests for camera relocalization in rgb-d images. In CVPR, 2013

    2013

  50. [58]

    Nrgbd: A large-scale dataset for novel view synthesis and 3d reconstruc- tion from rgb-d images

    Guandao Yang et al. Nrgbd: A large-scale dataset for novel view synthesis and 3d reconstruc- tion from rgb-d images. In NeurIPS Datasets and Benchmarks, 2023

    2023

  51. [59]

    A benchmark for the evaluation of rgb-d slam systems

    J"urgen Sturm, Nikolas Engelhard, Felix Endres, Wolfram Burgard, and Daniel Cremers. A benchmark for the evaluation of rgb-d slam systems. IROS, 2012

    2012

  52. [60]

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

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

    2023

  53. [61]

    Image quality metrics: Psnr vs

    Aljoscha Hore and Djemel Ziou. Image quality metrics: Psnr vs. ssim. Pattern Recognition, 2010

    2010

  54. [62]

    Bovik, Hamid R

    Zhou Wang, Alan C. Bovik, Hamid R. Sheikh, and Eero P. Simoncelli. Image quality assess- ment: From error visibility to structural similarity. IEEE Transactions on Image Processing, 2004

    2004

  55. [63]

    Efros, Eli Shechtman, and Oliver Wang

    Richard Zhang, Phillip Isola, Alexei A. Efros, Eli Shechtman, and Oliver Wang. The unrea- sonable effectiveness of deep features as a perceptual metric. In CVPR, 2018

    2018

  56. [64]

    Apollo: Sgd-like memory, adamw-level performance

    Hanqing Zhu, Zhenyu Zhang, Wenyan Cong, Xi Liu, Sem Park, Vikas Chandra, Bo Long, David Z Pan, Zhangyang Wang, and Jinwon Lee. Apollo: Sgd-like memory, adamw-level performance. arXiv preprint arXiv:2412.05270, 2024

    2024 arXiv

  57. [65]

    Unsupervised learning of depth and ego-motion from video

    Tinghui Zhou and et al. Unsupervised learning of depth and ego-motion from video. In CVPR, 2017

    2017

  58. [66]

    D3vo: Deep depth, deep pose and deep uncertainty for monocular visual odometry

    Nan Yang, Rui Wang, and Daniel Cremers. D3vo: Deep depth, deep pose and deep uncertainty for monocular visual odometry. In CVPR, 2020

    2020

  59. [67]

    Ultrra: A benchmark for air-ground relative pose estimation

    Zihan Wang et al. Ultrra: A benchmark for air-ground relative pose estimation. In CVPR, 2024

    2024

  60. [68]

    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

    2024 arXiv

  61. [69]

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

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

    2022

  62. [70]

    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 Brégier, Gabriela Csurka, Leonid Antsfeld, Boris Chidlovskii, and Jérôme Revaud. CroCo v2: Improved Cross-view Completion Pre-training for Stereo Matching and Optical Flow. In ICCV, 2023. 13

    2023

  63. [71]

    Stereo4d: Learning how things move in 3d from internet stereo videos

    Linyi Jin, Richard Tucker, Zhengqi Li, David Fouhey, Noah Snavely, and Aleksander Holynski. Stereo4d: Learning how things move in 3d from internet stereo videos. In CVPR, 2025

    2025

  64. [72]

    Uni4d: Unifying visual foundation models for 4d modeling from a single video

    David Yifan Yao, Albert J Zhai, and Shenlong Wang. Uni4d: Unifying visual foundation models for 4d modeling from a single video. In Proceedings of the Computer Vision and Pattern Recognition Conference, pages 1116–1126, 2025

    2025

  65. [73]

    Zero-shot monocular scene flow estimation in the wild

    Yiqing Liang, Abhishek Badki, Hang Su, James Tompkin, and Orazio Gallo. Zero-shot monocular scene flow estimation in the wild. In CVPR, 2025

    2025

  66. [74]

    Dynamic point maps: A versatile representation for dynamic 3d reconstruction

    Edgar Sucar, Zihang Lai, Eldar Insafutdinov, and Andrea Vedaldi. Dynamic point maps: A versatile representation for dynamic 3d reconstruction. arXiv preprint arXiv:2503.16318, 2025

    2025 arXiv

  67. [75]

    Dˆ 2ust3r: Enhancing 3d reconstruction with 4d pointmaps for dynamic scenes

    Jisang Han, Honggyu An, Jaewoo Jung, Takuya Narihira, Junyoung Seo, Kazumi Fukuda, Chaehyun Kim, Sunghwan Hong, Yuki Mitsufuji, and Seungryong Kim. Dˆ 2ust3r: Enhancing 3d reconstruction with 4d pointmaps for dynamic scenes. arXiv preprint arXiv:2504.06264, 2025

    2025

  68. [76]

    Learning multi-frame and monocular prior for estimating geometry in dynamic scenes

    Seong Hyeon Park and Jinwoo Shin. Learning multi-frame and monocular prior for estimating geometry in dynamic scenes. arXiv preprint arXiv:2505.01737, 2025

    2025 arXiv

  69. [77]

    Pomato: Marrying pointmap matching with temporal motion for dynamic 3d reconstruction

    Songyan Zhang, Yongtao Ge, Jinyuan Tian, Guangkai Xu, Hao Chen, Chen Lv, and Chun- hua Shen. Pomato: Marrying pointmap matching with temporal motion for dynamic 3d reconstruction. arXiv preprint arXiv:2504.05692, 2025

    2025 arXiv

  70. [78]

    Back on track: Bundle adjustment for dynamic scene reconstruction

    Weirong Chen, Ganlin Zhang, Felix Wimbauer, Rui Wang, Nikita Araslanov, Andrea Vedaldi, and Daniel Cremers. Back on track: Bundle adjustment for dynamic scene reconstruction. arXiv preprint arXiv:2504.14516, 2025

    2025

  71. [79]

    Storm: Spatio-temporal reconstruction model for large-scale outdoor scenes

    Jiawei Yang, Jiahui Huang, Yuxiao Chen, Yan Wang, Boyi Li, Yurong You, Apoorva Sharma, Maximilian Igl, Peter Karkus, Danfei Xu, et al. Storm: Spatio-temporal reconstruction model for large-scale outdoor scenes. arXiv preprint arXiv:2501.00602, 2024

    2024 arXiv

  72. [80]

    Dynamic point maps: A versatile representation for dynamic 3d reconstruction, 2025

    Edgar Sucar, Zihang Lai, Eldar Insafutdinov, and Andrea Vedaldi. Dynamic point maps: A versatile representation for dynamic 3d reconstruction, 2025

    2025

  73. [81]

    Regist3r: Incremental registration with stereo foundation model

    Sidun Liu, Wenyu Li, Peng Qiao, and Yong Dou. Regist3r: Incremental registration with stereo foundation model. arXiv preprint arXiv:2504.12356, 2025

    2025

  74. [82]

    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

    2024 arXiv

  75. [83]

    Must3r: Multi-view network for stereo 3d reconstruction

    Yohann Cabon, Lucas Stoffl, Leonid Antsfeld, Gabriela Csurka, Boris Chidlovskii, Jerome Revaud, and Vincent Leroy. Must3r: Multi-view network for stereo 3d reconstruction. In Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR) , pages 1050–1060, June 2025

    2025

  76. [84]

    Light3r-sfm: Towards feed-forward structure- from-motion

    Sven Elflein, Qunjie Zhou, and Laura Leal-Taixé. Light3r-sfm: Towards feed-forward structure- from-motion. In Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR), pages 16774–16784, June 2025

    2025

  77. [85]

    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. In Proceedings of the Computer Vision and Pattern Recognition Conference, pages 5283–5293, 2025

    2025

  78. [86]

    Spatialsplat: Efficient semantic 3d from sparse unposed images

    Yu Sheng, Jiajun Deng, Xinran Zhang, Yu Zhang, Bei Hua, Yanyong Zhang, and Jian- min Ji. Spatialsplat: Efficient semantic 3d from sparse unposed images. arXiv preprint arXiv:2505.23044, 2025

    2025

  79. [87]

    Matrix3d: Large photogrammetry model all-in-one

    Yuanxun Lu, Jingyang Zhang, Tian Fang, Jean-Daniel Nahmias, Yanghai Tsin, Long Quan, Xun Cao, Yao Yao, and Shiwei Li. Matrix3d: Large photogrammetry model all-in-one. arXiv preprint arXiv:2502.07685, 2025. 14

    2025 arXiv

  80. [88]

    Anysplat: Feed-forward 3d gaussian splatting from unconstrained views

    Lihan Jiang, Yucheng Mao, Linning Xu, Tao Lu, Kerui Ren, Yichen Jin, Xudong Xu, Mulin Yu, Jiangmiao Pang, Feng Zhao, et al. Anysplat: Feed-forward 3d gaussian splatting from unconstrained views. arXiv preprint arXiv:2505.23716, 2025

    2025

  81. [89]

    Vicasplat: A single run is all you need for 3d gaussian splatting and camera estimation from unposed video frames

    Zhiqi Li, Chengrui Dong, Yiming Chen, Zhangchi Huang, and Peidong Liu. Vicasplat: A single run is all you need for 3d gaussian splatting and camera estimation from unposed video frames. arXiv preprint arXiv:2503.10286, 2025

    2025 arXiv

  82. [90]

    Pref3r: Pose-free feed-forward 3d gaussian splatting from variable-length image sequence

    Zequn Chen, Jiezhi Yang, and Heng Yang. Pref3r: Pose-free feed-forward 3d gaussian splatting from variable-length image sequence. arXiv preprint arXiv:2411.16877, 2024

    2024 arXiv

  83. [91]

    Pow3r: Empowering unconstrained 3d reconstruction with camera and scene priors

    Wonbong Jang, Philippe Weinzaepfel, Vincent Leroy, Lourdes Agapito, and Jerome Revaud. Pow3r: Empowering unconstrained 3d reconstruction with camera and scene priors. arXiv preprint arXiv:2503.17316, 2025

    2025 arXiv

  84. [92]

    Spurfies: Sparse surface reconstruction using local geometry priors, 2024

    Kevin Raj, Christopher Wewer, Raza Yunus, Eddy Ilg, and Jan Eric Lenssen. Spurfies: Sparse surface reconstruction using local geometry priors, 2024

    2024

  85. [93]

    Towards in-the-wild 3d plane reconstruction from a single image

    Jiachen Liu, Rui Yu, Sili Chen, Sharon X Huang, and Hengkai Guo. Towards in-the-wild 3d plane reconstruction from a single image. In Proceedings of the Computer Vision and Pattern Recognition Conference, pages 27027–27037, 2025

    2025

  86. [94]

    MegaSaM: Accurate, fast and robust structure and motion from casual dynamic videos

    Zhengqi Li, Richard Tucker, Forrester Cole, Qianqian Wang, Linyi Jin, Vickie Ye, Angjoo Kanazawa, Aleksander Holynski, and Noah Snavely. MegaSaM: Accurate, fast and robust structure and motion from casual dynamic videos. In CVPR, 2025

    2025

  87. [95]

    Vggt-slam: Dense rgb slam optimized on the sl (4) manifold

    Dominic Maggio, Hyungtae Lim, and Luca Carlone. Vggt-slam: Dense rgb slam optimized on the sl (4) manifold. arXiv preprint arXiv:2505.12549, 2025

    2025 arXiv

  88. [96]

    Riku Murai, Eric Dexheimer, and Andrew J. Davison. Mast3r-slam: Real-time dense slam with 3d reconstruction priors. In Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR), pages 16695–16705, June 2025

    2025

  89. [97]

    Wildgs-slam: Monocular gaussian splatting slam in dynamic environments

    Jianhao Zheng, Zihan Zhu, Valentin Bieri, Marc Pollefeys, Songyou Peng, and Iro Armeni. Wildgs-slam: Monocular gaussian splatting slam in dynamic environments. In Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR), pages 11461–11471, June 2025

    2025

  90. [98]

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

    Yuzheng Liu, Siyan Dong, Shuzhe Wang, Yingda Yin, Yanchao Yang, Qingnan Fan, and Baoquan Chen. Slam3r: Real-time dense scene reconstruction from monocular rgb videos. In Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR), pages 16651–16662, June 2025

    2025

  91. [99]

    Hier-slam++: Neuro-symbolic semantic slam with a hierarchically categorical gaussian splatting

    Boying Li, Vuong Chi Hao, Peter J Stuckey, Ian Reid, and Hamid Rezatofighi. Hier-slam++: Neuro-symbolic semantic slam with a hierarchically categorical gaussian splatting. arXiv preprint arXiv:2502.14931, 2025

    2025 arXiv

  92. [100]

    Driv3r: Learning dense 4d reconstruction for autonomous driving

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

    2024 arXiv

  93. [101]

    Rig3r: Rig-aware conditioning for learned 3d reconstruction

    Samuel Li, Pujith Kachana, Prajwal Chidananda, Saurabh Nair, Yasutaka Furukawa, and Matthew Brown. Rig3r: Rig-aware conditioning for learned 3d reconstruction. arXiv preprint arXiv:2506.02265, 2025

    2025 arXiv

  94. [102]

    Reconstructing people, places, and cameras

    Lea Müller, Hongsuk Choi, Anthony Zhang, Brent Yi, Jitendra Malik, and Angjoo Kanazawa. Reconstructing people, places, and cameras. In Proceedings of the Computer Vision and Pattern Recognition Conference, pages 21948–21958, 2025

    2025

  95. [103]

    Joint optimization for 4d human-scene reconstruction in the wild

    Zhizheng Liu, Joe Lin, Wayne Wu, and Bolei Zhou. Joint optimization for 4d human-scene reconstruction in the wild. arXiv preprint arXiv:2501.02158, 2025. 15

    2025 arXiv

  96. [104]

    Vlm-3r: Vision-language models augmented with instruction-aligned 3d reconstruction, 2025

    Zhiwen Fan, Jian Zhang, Renjie Li, Junge Zhang, Runjin Chen, Hezhen Hu, Kevin Wang, Huaizhi Qu, Dilin Wang, Zhicheng Yan, Hongyu Xu, Justin Theiss, Tianlong Chen, Jiachen Li, Zhengzhong Tu, Zhangyang Wang, and Rakesh Ranjan. Vlm-3r: Vision-language models augmented with instru...

    2025

  97. [105]

    Zhang, Deva Ramanan, and Shubham Tulsiani

    Qitao Zhao, Amy Lin, Jeff Tan, Jason Y . Zhang, Deva Ramanan, and Shubham Tulsiani. Diffusionsfm: Predicting structure and motion via ray origin and endpoint diffusion. In Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR) , pages 6317–6326, June 2025

    2025

  98. [106]

    Unigeo: Taming video diffusion for unified consistent geometry estimation

    Yang-Tian Sun, Xin Yu, Zehuan Huang, Yi-Hua Huang, Yuan-Chen Guo, Ziyi Yang, Yan-Pei Cao, and Xiaojuan Qi. Unigeo: Taming video diffusion for unified consistent geometry estimation. arXiv preprint arXiv:2505.24521, 2025

    2025 arXiv

  99. [107]

    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 conference on computer vision and pattern recognition, pages 21686–21697, 2024

    2024

  100. [108]

    Flowmap: High-quality camera poses, intrinsics, and depth via gradient descent

    Cameron Smith, David Charatan, Ayush Tewari, and Vincent Sitzmann. Flowmap: High-quality camera poses, intrinsics, and depth via gradient descent. arXiv preprint arXiv:2404.15259, 2024

    2024 arXiv

  101. [109]

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

    René 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

  102. [110]

    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

    2024

  103. [111]

    Depth anything v2

    Lihe Yang, Bingyi Kang, Zilong Huang, Zhen Zhao, Xiaogang Xu, Jiashi Feng, and Heng- shuang Zhao. Depth anything v2. arXiv:2406.09414, 2024

    2024 arXiv

  104. [112]

    Repurposing diffusion-based image generators for monocular depth estimation

    Bingxin Ke, Anton Obukhov, Shengyu Huang, Nando Metzger, Rodrigo Caye Daudt, and Kon- rad Schindler. Repurposing diffusion-based image generators for monocular depth estimation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9492–9502, 2024

    2024

  105. [113]

    Fine-tuning image-conditional diffusion models is easier than you think

    Gonzalo Martin Garcia, Karim Abou Zeid, Christian Schmidt, Daan De Geus, Alexander Hermans, and Bastian Leibe. Fine-tuning image-conditional diffusion models is easier than you think. In 2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), pages 753–762. ...

    2025

  106. [114]

    Geowizard: Unleashing the diffusion priors for 3d geometry estimation from a single image

    Xiao Fu, Wei Yin, Mu Hu, Kaixuan Wang, Yuexin Ma, Ping Tan, Shaojie Shen, Dahua Lin, and Xiaoxiao Long. Geowizard: Unleashing the diffusion priors for 3d geometry estimation from a single image. In European Conference on Computer Vision, pages 241–258. Springer, 2024

    2024

  107. [115]

    Depthfm: Fast monocular depth estimation with flow matching

    Ming Gui, Johannes Schusterbauer, Ulrich Prestel, Pingchuan Ma, Dmytro Kotovenko, Olga Grebenkova, Stefan Andreas Baumann, Vincent Tao Hu, and Björn Ommer. Depthfm: Fast monocular depth estimation with flow matching. arXiv preprint arXiv:2403.13788, 2024

    2024 arXiv

  108. [116]

    Lotus: Diffusion-based visual foundation model for high-quality dense prediction

    Jing He, Haodong Li, Wei Yin, Yixun Liang, Leheng Li, Kaiqiang Zhou, Hongbo Zhang, Bingbing Liu, and Ying-Cong Chen. Lotus: Diffusion-based visual foundation model for high-quality dense prediction. arXiv preprint arXiv:2409.18124, 2024

    2024 arXiv

  109. [117]

    Sharpdepth: Sharpening metric depth predictions using diffusion distillation

    Duc-Hai Pham, Tung Do, Phong Nguyen, Binh-Son Hua, Khoi Nguyen, and Rang Nguyen. Sharpdepth: Sharpening metric depth predictions using diffusion distillation. arXiv preprint arXiv:2411.18229, 2024

    2024 arXiv

  110. [118]

    Con- sistent depth of moving objects in video

    Zhoutong Zhang, Forrester Cole, Richard Tucker, William T Freeman, and Tali Dekel. Con- sistent depth of moving objects in video. ACM Transactions on Graphics (ToG), 40(4):1–12, 2021. 16

    2021

  111. [119]

    Mamo: Leveraging memory and attention for monocular video depth estimation

    Rajeev Yasarla, Hong Cai, Jisoo Jeong, Yunxiao Shi, Risheek Garrepalli, and Fatih Porikli. Mamo: Leveraging memory and attention for monocular video depth estimation. In Pro- ceedings of the IEEE/CVF International Conference on Computer Vision, pages 8754–8764, 2023

    2023

  112. [120]

    Neural video depth stabilizer

    Yiran Wang, Min Shi, Jiaqi Li, Zihao Huang, Zhiguo Cao, Jianming Zhang, Ke Xian, and Guosheng Lin. Neural video depth stabilizer. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 9466–9476, 2023

    2023

  113. [121]

    Robust consistent video depth estimation

    Johannes Kopf, Xuejian Rong, and Jia-Bin Huang. Robust consistent video depth estimation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1611–1621, 2021

    2021

  114. [122]

    Consistent video depth estimation

    Xuan Luo, Jia-Bin Huang, Richard Szeliski, Kevin Matzen, and Johannes Kopf. Consistent video depth estimation. ACM Transactions on Graphics (ToG), 39(4):71–1, 2020

    2020

  115. [123]

    Learning temporally consistent video depth from video diffusion priors

    Jiahao Shao, Yuanbo Yang, Hongyu Zhou, Youmin Zhang, Yujun Shen, Vitor Guizilini, Yue Wang, Matteo Poggi, and Yiyi Liao. Learning temporally consistent video depth from video diffusion priors. arXiv preprint arXiv:2406.01493, 2024

    2024 arXiv

  116. [124]

    Learning to recover 3d scene shape from a single image

    Wei Yin, Jianming Zhang, Oliver Wang, Simon Niklaus, Long Mai, Simon Chen, and Chunhua Shen. Learning to recover 3d scene shape from a single image. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 204–213, 2021

    2021

  117. [125]

    Towards accurate reconstruction of 3d scene shape from a single monocular image

    Wei Yin, Jianming Zhang, Oliver Wang, Simon Niklaus, Simon Chen, Yifan Liu, and Chunhua Shen. Towards accurate reconstruction of 3d scene shape from a single monocular image. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(5):6480–6494, 2022

    2022

  118. [126]

    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 Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10106–10116, 2024

    2024

  119. [127]

    Depth pro: Sharp monocular metric depth in less than a second

    Aleksei Bochkovskii, Amaà G, l Delaunoy, Hugo Germain, Marcel Santos, Yichao Zhou, Stephan R Richter, and Vladlen Koltun. Depth pro: Sharp monocular metric depth in less than a second. arXiv preprint arXiv:2410.02073, 2024

    2024 arXiv

  120. [128]

    Moge: Unlocking accurate monocular geometry estimation for open-domain images with optimal training supervision

    Ruicheng Wang, Sicheng Xu, Cassie Dai, Jianfeng Xiang, Yu Deng, Xin Tong, and Jiaolong Yang. Moge: Unlocking accurate monocular geometry estimation for open-domain images with optimal training supervision. arXiv preprint arXiv:2410.19115, 2024

    arXiv

  121. [129]

    Metric3d: Towards zero-shot metric 3d prediction from a single image

    Wei Yin, Chi Zhang, Hao Chen, Zhipeng Cai, Gang Yu, Kaixuan Wang, Xiaozhi Chen, and Chunhua Shen. Metric3d: Towards zero-shot metric 3d prediction from a single image. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 9043–9053, 2023

    2023

  122. [130]

    Metric3d v2: A versatile monocular geometric 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 geometric foundation model for zero-shot metric depth and surface normal estimation. IEEE Transactions on Pattern Analysis...

    2024

  123. [131]

    Pixelsplat: 3d gaussian splats from image pairs for scalable generalizable 3d reconstruction

    David Charatan, Sizhe Lester Li, Andrea Tagliasacchi, and Vincent Sitzmann. Pixelsplat: 3d gaussian splats from image pairs for scalable generalizable 3d reconstruction. 2024 ieee. In CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 19457–19467, 2023

    2024

  124. [132]

    Mvsplat: Efficient 3d gaussian splatting from sparse multi-view images

    Yuedong Chen, Haofei Xu, Chuanxia Zheng, Bohan Zhuang, Marc Pollefeys, Andreas Geiger, Tat-Jen Cham, and Jianfei Cai. Mvsplat: Efficient 3d gaussian splatting from sparse multi-view images. In European Conference on Computer Vision, pages 370–386. Springer, 2024

    2024

  125. [133]

    Droid: A large-scale in-the-wild robot manipulation dataset

    Alexander Khazatsky, Karl Pertsch, Suraj Nair, Ashwin Balakrishna, Sudeep Dasari, Siddharth Karamcheti, Soroush Nasiriany, Mohan Kumar Srirama, Lawrence Yunliang Chen, Kirsty Ellis, et al. Droid: A large-scale in-the-wild robot manipulation dataset. arXiv preprint arXiv:2403.1...

    2024 arXiv

  126. [134]

    Sekai: A video dataset towards world exploration

    Zhen Li, Chuanhao Li, Xiaofeng Mao, Shaoheng Lin, Ming Li, Shitian Zhao, Zhaopan Xu, Xinyue Li, Yukang Feng, Jianwen Sun, et al. Sekai: A video dataset towards world exploration. arXiv preprint arXiv:2506.15675, 2025

    2025

  127. [135]

    Spatial-mllm: Boosting mllm capabilities in visual-based spatial intelligence

    Diankun Wu, Fangfu Liu, Yi-Hsin Hung, and Yueqi Duan. Spatial-mllm: Boosting mllm capabilities in visual-based spatial intelligence. arXiv preprint arXiv:2505.23747, 2025

    2025 arXiv

  128. [136]

    Mllms need 3d-aware representation supervision for scene understanding

    Xiaohu Huang, Jingjing Wu, Qunyi Xie, and Kai Han. Mllms need 3d-aware representation supervision for scene understanding. arXiv preprint arXiv:2506.01946, 2025

    2025

  129. [137]

    From thousands to billions: 3d visual language grounding via render-supervised distillation from 2d vlms

    Ang Cao, Sergio Arnaud, Oleksandr Maksymets, Jianing Yang, Ayush Jain, Ada Martin, Vincent-Pierre Berges, Paul McVay, Ruslan Partsey, Aravind Rajeswaran, et al. From thousands to billions: 3d visual language grounding via render-supervised distillation from 2d vlms. In Forty-s...

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.