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REVIEW 4 major objections 4 minor 1 cited by

WorldMirror: Universal 3D World Reconstruction with Any-Prior Prompting

T0 review · 4 major / 4 minor · reviewed 2026-08-04 · deepseek-v4-flash

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Pith's one-line read WorldMirror proposes a single feed-forward model that accepts any available geometric priors—camera poses, intrinsics, depth maps—and simultaneously outputs point clouds, depth, normals, camera parameters, and renderable 3D Gaussians, with

desk verdict Solid multi-task 3D reconstruction system with convincing prior-injection results, but the headline NVS numbers use GT cameras for back-projection, undercutting the pose-free SOTA claim. read the letter →

arxiv 2510.10726 v2 pith:YRTS3FJ7 submitted 2025-10-12 cs.CV

Yifan Liu , Zhiyuan Min , Zhenwei Wang , Junta Wu , Tengfei Wang , Yixuan Yuan , Yawei Luo , Chunchao Guo This is my paper · ORCID
classification cs.CV
keywords universal3Dreconstructionfeed-forwardgeometrypredictionmulti-modalpriorpromptingcameraposeestimationdepthsurfacenormalnovelviewsynthesisGaussiansplatting
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper tries to establish that one architecture can be a universal 3D geometry engine: given a handful of images and whatever geometric hints are available (calibrated intrinsics, camera poses, depth maps, or none), it predicts dense point maps, multi-view depth, camera parameters, surface normals, and 3D Gaussians in a single forward pass. The central claim is that input flexibility and multi-task prediction are mutually reinforcing—injecting any single prior improves not just the corresponding output but all geometric outputs, and injecting all priors gives the largest gains. If true, a single model could replace several specialized pipelines (depth estimators, pose estimators, normal predictors, novel-view renderers) and would naturally exploit sensor data that real systems often have. The paper reports state-of-the-art numbers on point-map, camera, depth, normal, and novel-view benchmarks while keeping feed-forward speed, and it shows the predicted geometry also accelerates downstream per-scene optimization.

What carries the argument

Multi-Modal Prior Prompting: camera poses (rotation as quaternion plus normalized translation) and intrinsics (normalized focal lengths and principal point) are each compressed into a single token and concatenated with image tokens; depth maps are normalized and turned into dense tokens that are added directly to spatially aligned image tokens. During training a dynamic injection scheme randomly zeros out each prior with probability 0.5, so the model learns to exploit whichever priors are present and degrade gracefully when none are. A shared transformer backbone feeds several dense prediction heads that output point maps, depth, surface normals, and 3D Gaussians, plus a camera head; 3D Gaus

What would settle it

Re-run the novel-view benchmark with only the model's predicted cameras and intrinsics used to back-project Gaussian centers, keeping all other settings fixed. If the margin over the previous best feed-forward splatting method collapses, the pose-free SOTA claim fails. Separately, to test the mutual-reinforcement thesis, ablate each prior one at a time on a held-out zero-shot benchmark and check that each prior improves every output task; if a depth prior improves only depth, the 'universal gains' claim is falsified.

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Extended reading notes

Core claim

The core discovery, stated on the paper's own terms, is that a model which can consume any subset of geometric priors and simultaneously predict many 3D representations learns to use prior information as a shared scaffold: a depth hint sharpens camera estimates, a pose hint clarifies depth, and so on. The authors show that this transfer is systematic—single-modality priors give dual benefits (improving the corresponding task and the rest), and the full prior set yields the best point maps, depths, normals, and renderings. They also find that compact global embeddings for poses and intrinsics (one token per view) outperform dense per-pixel conditioning, and that a curriculum from simple to co

Load-bearing premise

The load-bearing premise is that the headline novel-view numbers represent the pose-free system; in fact those numbers are produced with ground-truth camera poses and intrinsics, so the pose-free claim rests entirely on the 'w/o GT Cameras' ablation row in Table 6.

Editorial extensions

If this is right

  • A single model can serve as a common geometry backbone for AR, robotics, and content creation, since it outputs point clouds, camera poses, depth, normals, and renderable splats in one pass without per-scene optimization.
  • Any available prior—even one modality—should improve all downstream geometric tasks, so systems with RGB-D sensors, SLAM poses, or known calibration can expect a uniform quality boost rather than a task-specific one.
  • Providing all three priors (intrinsics, poses, depth) yields the largest measured gains, implying that sensor-rich pipelines obtain the biggest benefit.
  • The predicted point cloud and Gaussians serve as a strong initialization for per-scene 3D Gaussian optimization, reaching better rendering quality in fewer iterations than random initialization.
  • Because the model degrades gracefully when no priors are given, it can be deployed in settings where sensor information is sometimes unavailable.

Reading between the lines

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

  • If the universal-gain result is genuine, it implies the network is forming a shared internal scene model rather than task-specific shortcuts; a direct test would be to hold out a prior type never seen in training (e.g., sparse LiDAR depth) and check that it still lifts all tasks.
  • The pose-free novel-view claim should be read through the 'w/o GT Cameras' ablation, not the headline Table 4 numbers, because those are produced with ground-truth cameras for back-projection; re-running the full benchmark with only predicted cameras would settle how much of the reported margin is actually pose-free.
  • The finding that compact single-token embeddings beat dense ray embeddings suggests the priors act as global conditioning rather than per-pixel constraints; one consequence is that noisy or approximate priors (e.g., from an onboard SLAM) may be nearly as effective as exact ones—testable by injecting perturbed poses.
  • The measured marginal gains per prior could be turned into an active-sensing policy: a robot could query the cheapest prior (e.g., intrinsics) first and only request depth or poses when the expected geometric gain justifies the sensor cost.
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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

4 major / 4 minor

Summary. WorldMirror presents a single feed-forward transformer that consumes N multi-view images together with any subset of geometric priors (camera poses, intrinsics, per-view depth) and predicts point maps, multi-view depth, camera parameters, surface normals, and 3D Gaussians. Priors are encoded as tokens (compact tokens for camera/intrinsics, dense additive tokens for depth), randomly dropped during training, and a curriculum schedules tasks, data, and resolution. Experiments cover point maps (7-Scenes/NRGBD/DTU), cameras (RealEstate10K/Sintel/TUM), normals (ScanNet/NYU/iBims), depth (NYU/Sintel/KITTI), and NVS (RealEstate10K/DL3DV/VR-NeRF), with claims of state-of-the-art results over VGGT, π3, AnySplat, FLARE, and others, plus universal gains from any prior injection.

Significance. If the results hold, WorldMirror is a useful step toward a generalist 3D geometric foundation model: one architecture that handles camera, depth, normal, point-map, and NVS tasks, with a clean token-based mechanism for incorporating optional priors. The breadth of the evaluation is a genuine strength, as is the ablation showing that a single-token prior embedding outperforms dense embeddings. The paper also demonstrates generalization to AI-generated video inputs. The main concerns are evaluative: the headline NVS numbers use ground-truth cameras for Gaussian back-projection, the DL3DV zero-shot status needs clarification, and the 'any-prior' claim lacks a depth-prior NVS ablation. These are fixable, and the Table 6 pose-free ablation suggests the central NVS conclusion may survive a corrected protocol, but the paper as written overstates the pose-free SOTA claim.

major comments (4)
  1. [Sec. 3.2, Eq. (4); Table 4] The headline NVS comparison is not generated by a fully pose-free system: Eq. (4) back-projects predicted depth maps using ground-truth camera poses and intrinsics. The Table 6 'w/o GT Cameras' ablation substitutes predicted cameras and reports small drops on RealEstate10K (20.30 vs 20.29) but larger drops on DL3DV (20.69 vs 20.91) and VR-NeRF (24.76 vs 25.75), and is not compared against AnySplat/FLARE under the same protocol. Please report Table 4 with predicted cameras and include baselines under that protocol, or explicitly scope the claims to 'given ground-truth cameras'.
  2. [Sec. 4.1; Sec. A.2] DL3DV is listed in the training data (A.2) and also used for 'zero-shot' NVS evaluation in Table 4. The paper does not state that the FLARE test split is disjoint from the DL3DV training scenes. If any overlap exists, the DL3DV rows are not zero-shot and may favor methods trained on that dataset. Please provide an explicit overlap analysis or re-evaluate on a held-out set.
  3. [Sec. 4.1; Table 4; Abstract] The paper claims 'any-prior prompting' and universal gains from priors, but Table 4 reports NVS with image-only, intrinsics, camera pose, and intrinsics+camera-pose inputs; no depth-prior NVS row is reported. Since depth is the most directly relevant prior for Gaussian placement, its absence weakens the 'any-prior' claim. Please add a depth-prior (and depth+other) NVS ablation, or explain why depth is not used in this setting.
  4. [Sec. 4.1, NVS paragraph] The phrase 'we follow the test-time camera pose alignment introduced by AnySplat' is ambiguous. If this alignment involves fitting predicted cameras to ground-truth cameras (e.g., a similarity transform), then even the 'w/o GT Cameras' variant leaks ground-truth pose information. Please define exactly what is aligned, and state whether any ground-truth geometry is used after the forward pass.
minor comments (4)
  1. [Eq. (9)] The predicted surface normal in the normal loss is written as \hat{E}_i; this is the same symbol used for camera parameters and should be \hat{N}_i to avoid confusion.
  2. [Table 6] Title has a typo: 'Novel View Synthsis' should be 'Novel View Synthesis'.
  3. [Sec. 4.2, Fig. 6] The four metrics and their aggregation in Fig. 6 are only tersely described. Please spell out how the bars are computed and whether values are averaged over scenes or views.
  4. [Appendix C] The limitations section mentions dynamic scenes and maximum input resolution, but does not mention the ground-truth-camera NVS protocol or the DL3DV training/evaluation overlap. These should be acknowledged and addressed in the revision.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the reported results are empirical comparisons on external benchmarks; the GT-camera NVS protocol is an evaluation caveat, not a circular reduction.

full rationale

WorldMirror's central claims are empirical: it reports point-map, camera, depth, normal, and NVS numbers against external baselines on held-out datasets. The method is a learned multi-task regressor initialized from VGGT weights; no equation defines a predicted quantity in terms of the same quantity. The prior-injection ablations explicitly compare no-prior, single-prior, and all-prior configurations, so rows such as 'w/ depth' or 'w/ camera pose' are conditional-input settings, not fitted parameters renamed as predictions. The NVS pipeline in Eq. 4 places Gaussian centers using ground-truth [R|t] and K, and Table 4 is therefore not a fully pose-free evaluation; however, Table 6's 'w/o GT Cameras' ablation replaces them with predicted cameras and quantifies the drop, and this is a benchmark-protocol weakness rather than a circularity. The initialization from VGGT is disclosed and the comparison models are external. No self-citation chain is load-bearing, and no uniqueness or ansatz is imported from the authors' own prior work to force the architecture. Accordingly, the correct circularity verdict is 0.

Assumptions & free parameters 5 free parameters · 5 assumptions · 0 invented entities

The model relies on pretrained VGGT weights and large training data; the main free parameters are the composite loss weights and the prior-injection schedule. No new physical entities are introduced.

free parameters (5)
  • Composite loss weights = λ_points=1.0, λ_depth=1.0, λ_cam=5.0, λ_normal=1.0, λ_3dgs=1.0, λ_lpips=0.05, λ_gsdepth=0.1, λ_consis=0.1
    Hand-set weights in Eq. 6 (and A.1); no sensitivity analysis; all benchmark results depend on this tuning.
  • Prior dropout probability = 0.5
    Each prior modality is randomly disabled with p=0.5 during training (Sec. A.2); chosen by hand with no ablation on the probability.
  • Depth confidence quantile for L_consis = top 30%
    The confidence mask in Eq. 11 uses the top-30% quantile; no ablation or justification.
  • Curriculum schedule = 100 epochs (with normal head) + 50 epochs (Gaussian head fine-tune)
    Two-phase training schedule (Sec. A.2); impacts final performance and no ablation is provided for schedule choices.
  • Dynamic resolution range = 100k-250k pixels, aspect 0.5-2.0
    Training uses dynamic image resolution chosen by hand and only described briefly; affects generalization and evaluation.
assumptions (5)
  • domain assumption VGGT pretrained weights provide a valid initialization for WorldMirror.
    Sec. A.2 states the model is 'initialized from the pretrained weights of VGGT'. Improvements over VGGT may partly inherit VGGT's capabilities rather than the new components.
  • domain assumption Ground-truth camera poses and intrinsics are available at NVS inference time.
    Sec. 3.2, Eq. 4 uses ground-truth poses and intrinsics to back-project Gaussian centers. This is required for the reported NVS numbers and contradicts the paper's 'pose-free' framing.
  • domain assumption Pseudo-normals derived from GT depth via plane fitting are reliable enough to supervise the normal head.
    Sec. 3.2, surface normal estimation introduces this hybrid supervision for datasets lacking normal labels; no validation of pseudo-label quality is given.
  • domain assumption The DL3DV test split (FLARE split) does not overlap with the DL3DV training split used by WorldMirror.
    DL3DV is listed in training data (Sec. A.2), and the paper evaluates on the FLARE test split. No explicit overlap check is reported.
  • domain assumption Benchmark protocols (e.g., test-time camera pose alignment for NVS) are applied consistently to all methods.
    Sec. 4.1 NVS: metrics use AnySplat's test-time camera pose alignment. If baselines are not given the same alignment, comparisons could be skewed; the paper gives no assurance.

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Cite this review

Pith. "Pith review of WorldMirror: Universal 3D World Reconstruction with Any-Prior Prompting." pith.science (2026). https://pith.science/paper/YRTS3FJ7

@misc{pith2026251010726,
  author       = {Pith},
  title        = {Pith review of: WorldMirror: Universal 3D World Reconstruction with Any-Prior Prompting},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/YRTS3FJ7}},
  note         = {Machine review of arXiv:2510.10726}
}
read the original abstract

We present WorldMirror, a unified feed-forward model for comprehensive 3D geometric prediction tasks. Unlike existing methods constrained to image-only inputs or customized for a specific task, our framework flexibly integrates diverse geometric priors, including camera poses, intrinsics, and depth maps, while simultaneously generating multiple 3D representations: dense point clouds, multi-view depth maps, camera parameters, surface normals, and 3D Gaussians. Remarkably, prior injection yields universal gains across all tasks, suggesting that input flexibility and multi-task prediction are mutually reinforcing. WorldMirror achieves state-of-the-art performance across diverse benchmarks from camera, point map, depth, and surface normal estimation to novel view synthesis, while maintaining the efficiency of feed-forward inference.

Figures

Figures reproduced from arXiv: 2510.10726 by the authors.

Figure 1
Figure 1. WorldMirror is a large feed-forward 3D reconstruction model that takes raw images along with optional priors (depth, calibrated intrinsics, camera pose) as input and produces high-quality geometric attributes in seconds, including point clouds, 3DGS, cameras, depth, and normal maps. 1 INTRODUCTION Visual geometry learning is a fundamental problem in computer vision, with applications span￾ning augmented reality, rob… view at source ↗
Figure 2
Figure 2. Overview of WorldMirror. Given multi-view images with optional priors (depths, cali￾brated intrinsics, camera poses) as input, our framework encodes each prior modality into tokens and integrates them with image tokens. The composite tokens are subsequently processed by a visual transformer backbone to effectively aggregate multi-view features. The consolidated representations are then passed to multi-task heads to … view at source ↗
Figure 3
Figure 3. Feed-Forward 3D Gaussians Predicted by WorldMirror with In-The-Wild Inputs. Besides real photos, our method generalizes well to AI-created videos spanning diverse styles. Surface Normal Estimation. For surface normal estimation, we employ the same DPT architecture as other dense prediction tasks, followed by L2 normalization to ensure unit vector outputs: Nˆ i = DPTn(Tˆimg i ) / ||DPTn(Tˆimg i )||2. (3) To address t… view at source ↗
Figures from the paper (8 more)
Figure 4
Figure 4. Figure 4: Qualitative Comparisons of Novel View Synthesis. We compare with FLARE and AnySplat on RealEstate10K and DL3DV. The first four columns correspond to the sparse-view set￾ting, while the latter three correspond to the dense-view setting. Our approach surpasses baselines …
Figure 5
Figure 5. Figure 5: Geometric Priors Unlock Enhanced Scene Reconstruction of WorldMirror. (Top) Camera poses help the model to capture relative view positions accurately. (Middle) Calibrated intrinsic enhances the reconstruction by enabling precise projection modeling and geometry align￾m…
Figure 6
Figure 6. Figure 6: Geometric Priors Boosts Model’s Feed-Forward Performance across All Tasks. Incor￾porating a single modality not only enhances predictions for its corresponding task but also improves performance across other tasks. This suggests that modal information enables the model…
Figure 7
Figure 7. Figure 7: WorldMirror Improves Surface Reconstruction with Predicted Normal Maps. Novel View Synthesis Ablation. Tab. 6 reports ablation analysis on the novel view synthesis: (1) To examine the importance of using ground-truth camera parameters for novel view rendering, we repla…
Figure 8
Figure 8. Figure 8: Visual Results of Feed-Forward 3D Gaussians Generated by WorldMirror. D MORE VISUAL RESULTS D.1 NOVEL VIEW SYNTHESIS In [PITH_FULL_IMAGE:figures/full_fig_p018_8.png]
Figure 9
Figure 9. Figure 9: Visual Comparisons on 7-Scenes, NRGBD, and DTU datasets. WorldMirror delivers superior reconstruction fidelity compared to VGGT, effectively capturing spatial relationships within scenes while producing geometrically coherent structures. 19 [PITH_FULL_IMAGE:figures/fu…
Figure 10
Figure 10. Figure 10: Visual Comparisons of In-The-Wild Multi-View 3D Reconstruction. WorldMir￾ror delivers superior reconstruction fidelity with in-the-wild images as input, generating more plau￾sible results in challenging scenarios compared to VGGT. Our approach effectively resolves com…
Figure 11
Figure 11. Figure 11: More Qualitative Visualizations for Novel View Synthesis. “w/ prior” denotes our method with 3D priors from camera intrinsics and poses as inputs. 21 [PITH_FULL_IMAGE:figures/full_fig_p021_11.png]

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

  1. AirSplat: Alignment and Rating for Robust Feed-Forward 3D Gaussian Splatting

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Reference graph

Works this paper leans on

73 extracted references · 13 linked inside Pith · cited by 1 Pith paper

  1. [1]

    Map-free visual relocalization: Metric pose relative to a single image

    Eduardo Arnold, Jamie Wynn, Sara Vicente, Guillermo Garcia-Hernando, Aron Monszpart, Victor Prisacariu, Daniyar Turmukhambetov, and Eric Brachmann. Map-free visual relocalization: Metric pose relative to a single image. In European Conference on Computer Vision, pp.\ 690--708. Springer, 2022

    2022

  2. [2]

    Neural rgb-d surface reconstruction

    Dejan Azinovi \'c , Ricardo Martin-Brualla, Dan B Goldman, Matthias Nie ner, and Justus Thies. Neural rgb-d surface reconstruction. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.\ 6290--6301, 2022

    2022

  3. [3]

    Rethinking inductive biases for surface normal estimation

    Gwangbin Bae and Andrew J Davison. Rethinking inductive biases for surface normal estimation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.\ 9535--9545, 2024

    2024

  4. [4]

    Estimating and exploiting the aleatoric uncertainty in surface normal estimation

    Gwangbin Bae, Ignas Budvytis, and Roberto Cipolla. Estimating and exploiting the aleatoric uncertainty in surface normal estimation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp.\ 13137--13146, 2021

    2021

  5. [5]

    ARK itscenes - 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, and Elad Shulman. ARK itscenes - a diverse real-world dataset for 3d indoor scene understanding using mobile RGB -d data. In Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track...

    2021

  6. [6]

    Transformerfusion: Monocular rgb scene reconstruction using transformers

    Aljaz Bozic, Pablo Palafox, Justus Thies, Angela Dai, and Matthias Nie ner. Transformerfusion: Monocular rgb scene reconstruction using transformers. Advances in Neural Information Processing Systems, 34: 0 1403--1414, 2021

    2021

  7. [7]

    Matterport3d: Learning from rgb-d data in indoor environments

    Angel Chang, Angela Dai, Thomas Funkhouser, Maciej Halber, Matthias Niessner, Manolis Savva, Shuran Song, Andy Zeng, and Yinda Zhang. Matterport3d: Learning from rgb-d data in indoor environments. arXiv preprint arXiv:1709.06158, 2017

    arXiv 2017

  8. [8]

    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. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp.\ 19457--19467, 2024

    2024

Show all 73 references
  1. [9]

    Oasis: A large-scale dataset for single image 3d in the wild

    Weifeng Chen, Shengyi Qian, David Fan, Noriyuki Kojima, Max Hamilton, and Jia Deng. Oasis: A large-scale dataset for single image 3d in the wild. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.\ 679--688, 2020

    2020

  2. [10]

    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, pp.\ 370--386. Springer, 2024

    2024

  3. [11]

    Scannet: Richly-annotated 3d reconstructions of indoor scenes

    Angela Dai, Angel X Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, and Matthias Nie ner. Scannet: Richly-annotated 3d reconstructions of indoor scenes. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp.\ 5828--5839, 2017

    2017

  4. [12]

    Vggt-long: Chunk it, loop it, align it--pushing vggt's limits on kilometer-scale long rgb sequences

    Kai Deng, Zexin Ti, Jiawei Xu, Jian Yang, and Jin Xie. Vggt-long: Chunk it, loop it, align it--pushing vggt's limits on kilometer-scale long rgb sequences. arXiv preprint arXiv:2507.16443, 2025

    2025 arXiv

  5. [13]

    Omnidata: A scalable pipeline for making multi-task mid-level vision datasets from 3d scans

    Ainaz Eftekhar, Alexander Sax, Jitendra Malik, and Amir Zamir. Omnidata: A scalable pipeline for making multi-task mid-level vision datasets from 3d scans. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp.\ 10786--10796, 2021

    2021

  6. [14]

    Dens3r: A foundation model for 3d geometry prediction

    Xianze Fang, Jingnan Gao, Zhe Wang, Zhuo Chen, Xingyu Ren, Jiangjing Lyu, Qiaomu Ren, Zhonglei Yang, Xiaokang Yang, Yichao Yan, et al. Dens3r: A foundation model for 3d geometry prediction. arXiv preprint arXiv:2507.16290, 2025

    2025 arXiv

  7. [15]

    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, pp.\ 241--258. Springer, 2024

    2024

  8. [16]

    Cameractrl: Enabling camera control for text-to-video generation

    Hao He, Yinghao Xu, Yuwei Guo, Gordon Wetzstein, Bo Dai, Hongsheng Li, and Ceyuan Yang. Cameractrl: Enabling camera control for text-to-video generation. arXiv preprint arXiv:2404.02101, 2024

    2024 arXiv

  9. [17]

    Deepmvs: Learning multi-view stereopsis

    Po-Han Huang, Kevin Matzen, Johannes Kopf, Narendra Ahuja, and Jia-Bin Huang. Deepmvs: Learning multi-view stereopsis. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018

    2018

  10. [18]

    Voyager: Long-range and world-consistent video diffusion for explorable 3d scene generation

    Tianyu Huang, Wangguandong Zheng, Tengfei Wang, Yuhao Liu, Zhenwei Wang, Junta Wu, Jie Jiang, Hui Li, Rynson WH Lau, Wangmeng Zuo, et al. Voyager: Long-range and world-consistent video diffusion for explorable 3d scene generation. arXiv preprint arXiv:2506.04225, 2025

    2025 arXiv

  11. [19]

    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. In Proceedings of the Computer Vision and Pattern Recognition Conference, pp.\ 1071--1081, 2025

    2025

  12. [20]

    Large scale multi-view stereopsis evaluation

    Rasmus Jensen, Anders Dahl, George Vogiatzis, Engin Tola, and Henrik Aan s. Large scale multi-view stereopsis evaluation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp.\ 406--413, 2014

    2014

  13. [21]

    Leap: Liberate sparse-view 3d modeling from camera poses

    Hanwen Jiang, Zhenyu Jiang, Yue Zhao, and Qixing Huang. Leap: Liberate sparse-view 3d modeling from camera poses. arXiv preprint arXiv:2310.01410, 2023

    2023 arXiv

  14. [22]

    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

  15. [23]

    3d common corruptions and data augmentation

    O g uzhan Fatih Kar, Teresa Yeo, Andrei Atanov, and Amir Zamir. 3d common corruptions and data augmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.\ 18963--18974, 2022

    2022

  16. [24]

    Cotracker: It is better to track together

    Nikita Karaev, Ignacio Rocco, Benjamin Graham, Natalia Neverova, Andrea Vedaldi, and Christian Rupprecht. Cotracker: It is better to track together. In European conference on computer vision, pp.\ 18--35. Springer, 2024

    2024

  17. [25]

    Poisson surface reconstruction

    Michael Kazhdan, Matthew Bolitho, and Hugues Hoppe. Poisson surface reconstruction. In Proceedings of the fourth Eurographics symposium on Geometry processing, volume 7, 2006

    2006

  18. [26]

    3d gaussian splatting for real-time radiance field rendering

    Bernhard Kerbl, Georgios Kopanas, Thomas Leimk \"u hler, and George Drettakis. 3d gaussian splatting for real-time radiance field rendering. ACM Trans. Graph., 42 0 (4): 0 139--1, 2023

    2023

  19. [27]

    Evaluation of cnn-based single-image depth estimation methods

    Tobias Koch, Lukas Liebel, Friedrich Fraundorfer, and Marco Korner. Evaluation of cnn-based single-image depth estimation methods. In Proceedings of the European Conference on Computer Vision (ECCV) Workshops, pp.\ 0--0, 2018

    2018

  20. [28]

    Grounding image matching in 3d with mast3r

    Vincent Leroy, Yohann Cabon, and J \'e r \^o me Revaud. Grounding image matching in 3d with mast3r. In European Conference on Computer Vision, pp.\ 71--91. Springer, 2024

    2024

  21. [29]

    Megadepth: Learning single-view depth prediction from internet photos

    Zhengqi Li and Noah Snavely. Megadepth: Learning single-view depth prediction from internet photos. In Computer Vision and Pattern Recognition (CVPR), 2018

    2018

  22. [30]

    Dl3dv-10k: A large-scale scene dataset for deep learning-based 3d vision

    Lu Ling, Yichen Sheng, Zhi Tu, Wentian Zhao, Cheng Xin, Kun Wan, Lantao Yu, Qianyu Guo, Zixun Yu, Yawen Lu, et al. Dl3dv-10k: A large-scale scene dataset for deep learning-based 3d vision. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp...

    2024

  23. [31]

    Monosplat: Generalizable 3d gaussian splatting from monocular depth foundation models

    Yifan Liu, Keyu Fan, Weihao Yu, Chenxin Li, Hao Lu, and Yixuan Yuan. Monosplat: Generalizable 3d gaussian splatting from monocular depth foundation models. In Proceedings of the Computer Vision and Pattern Recognition Conference, pp.\ 21570--21579, 2025

    2025

  24. [32]

    Nerf: Representing scenes as neural radiance fields for view synthesis

    Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng. Nerf: Representing scenes as neural radiance fields for view synthesis. Communications of the ACM, 65 0 (1): 0 99--106, 2021

    2021

  25. [33]

    Epipolar-free 3d gaussian splatting for generalizable novel view synthesis

    Zhiyuan Min, Yawei Luo, Jianwen Sun, and Yi Yang. Epipolar-free 3d gaussian splatting for generalizable novel view synthesis. Advances in Neural Information Processing Systems, 37: 0 39573--39596, 2024

    2024

  26. [34]

    Aria digital twin: A new benchmark dataset for egocentric 3d machine perception

    Xiaqing Pan, Nicholas Charron, Yongqian Yang, Scott Peters, Thomas Whelan, Chen Kong, Omkar Parkhi, Richard Newcombe, and Yuheng Carl Ren. Aria digital twin: A new benchmark dataset for egocentric 3d machine perception. In Proceedings of the IEEE/CVF International Conference o...

    2023

  27. [35]

    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, pp.\ 10106--10116, 2024

    2024

  28. [36]

    Vision transformers for dense prediction

    Ren \'e Ranftl, Alexey Bochkovskiy, and Vladlen Koltun. Vision transformers for dense prediction. In Proceedings of the IEEE/CVF international conference on computer vision, pp.\ 12179--12188, 2021

    2021

  29. [37]

    Common 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. Common objects in 3d: Large-scale learning and evaluation of real-life 3d category reconstruction. In Proceedings of the IEEE/CVF international conference on computer visi...

    2021

  30. [38]

    Hypersim: A photorealistic synthetic dataset for holistic indoor scene understanding

    Mike Roberts, Jason Ramapuram, Anurag Ranjan, Atulit Kumar, Miguel Angel Bautista, Nathan Paczan, Russ Webb, and Joshua M Susskind. Hypersim: A photorealistic synthetic dataset for holistic indoor scene understanding. In Proceedings of the IEEE/CVF international conference on ...

    2021

  31. [39]

    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

  32. [40]

    Structure-from-motion revisited

    Johannes L Schonberger and Jan-Michael Frahm. Structure-from-motion revisited. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp.\ 4104--4113, 2016

    2016

  33. [41]

    Pixelwise view selection for unstructured multi-view stereo

    Johannes L Sch \"o nberger, Enliang Zheng, Jan-Michael Frahm, and Marc Pollefeys. Pixelwise view selection for unstructured multi-view stereo. In European conference on computer vision, pp.\ 501--518. Springer, 2016

    2016

  34. [42]

    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

  35. [43]

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

    Jamie Shotton, Ben Glocker, Christopher Zach, Shahram Izadi, Antonio Criminisi, and Andrew Fitzgibbon. Scene coordinate regression forests for camera relocalization in rgb-d images. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp.\ 2930--2937, 2013

    2013

  36. [44]

    Indoor segmentation and support inference from rgbd images

    Nathan Silberman, Derek Hoiem, Pushmeet Kohli, and Rob Fergus. Indoor segmentation and support inference from rgbd images. In European conference on computer vision, pp.\ 746--760. Springer, 2012

    2012

  37. [45]

    Splatt3r: Zero-shot gaussian splatting from uncalibrated image pairs

    Brandon Smart, Chuanxia Zheng, Iro Laina, and Victor Adrian Prisacariu. Splatt3r: Zero-shot gaussian splatting from uncalibrated image pairs. arXiv preprint arXiv:2408.13912, 2024

    2024 arXiv

  38. [46]

    A benchmark for the evaluation of rgb-d slam systems

    J \"u rgen 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, pp.\ 573--580. IEEE, 2012

    2012

  39. [47]

    Hunyuanworld 1.0: Generating immersive, explorable, and interactive 3d worlds from words or pixels

    HunyuanWorld Team. Hunyuanworld 1.0: Generating immersive, explorable, and interactive 3d worlds from words or pixels. arXiv preprint, 2025

    2025

  40. [48]

    Smd-nets: Stereo mixture density networks

    Fabio Tosi, Yiyi Liao, Carolin Schmitt, and Andreas Geiger. Smd-nets: Stereo mixture density networks. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp.\ 8942--8952, 2021

    2021

  41. [49]

    Posediffusion: Solving pose estimation via diffusion-aided bundle adjustment

    Jianyuan Wang, Christian Rupprecht, and David Novotny. Posediffusion: Solving pose estimation via diffusion-aided bundle adjustment. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp.\ 9773--9783, 2023 a

    2023

  42. [50]

    Vggt: Visual geometry grounded transformer

    Jianyuan Wang, Minghao Chen, Nikita Karaev, Andrea Vedaldi, Christian Rupprecht, and David Novotny. Vggt: Visual geometry grounded transformer. In Proceedings of the Computer Vision and Pattern Recognition Conference, pp.\ 5294--5306, 2025 a

    2025

  43. [51]

    Pf-lrm: Pose-free large reconstruction model for joint pose and shape prediction

    Peng Wang, Hao Tan, Sai Bi, Yinghao Xu, Fujun Luan, Kalyan Sunkavalli, Wenping Wang, Zexiang Xu, and Kai Zhang. Pf-lrm: Pose-free large reconstruction model for joint pose and shape prediction. arXiv preprint arXiv:2311.12024, 2023 b

    2023 arXiv

  44. [52]

    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 Proceedings of the Computer Vision and Pattern Recognition Conference, pp.\ 10510--10522, 2025 b

    2025

  45. [53]

    Dust3r: Geometric 3d vision made easy

    Shuzhe Wang, Vincent Leroy, Yohann Cabon, Boris Chidlovskii, and Jerome Revaud. Dust3r: Geometric 3d vision made easy. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.\ 20697--20709, 2024

    2024

  46. [54]

    Tartanair: A dataset to push the limits of visual slam

    Wenshan Wang, Delong Zhu, Xiangwei Wang, Yaoyu Hu, Yuheng Qiu, Chen Wang, Yafei Hu, Ashish Kapoor, and Sebastian Scherer. Tartanair: A dataset to push the limits of visual slam. In 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp.\ 4909--4916...

    2020

  47. [55]

    pi3: Scalable permutation-equivariant visual geometry learning

    Yifan Wang, Jianjun Zhou, Haoyi Zhu, Wenzheng Chang, Yang Zhou, Zizun Li, Junyi Chen, Jiangmiao Pang, Chunhua Shen, and Tong He. pi3: Scalable permutation-equivariant visual geometry learning. arXiv preprint arXiv:2507.13347, 2025 c

    2025 arXiv

  48. [56]

    Rgbd objects in the wild: Scaling real-world 3d object learning from rgb-d videos, 2024

    Hongchi Xia, Yang Fu, Sifei Liu, and Xiaolong Wang. Rgbd objects in the wild: Scaling real-world 3d object learning from rgb-d videos, 2024. URL https://arxiv.org/abs/2401.12592

    2024 arXiv

  49. [57]

    Depthsplat: Connecting gaussian splatting and depth

    Haofei Xu, Songyou Peng, Fangjinhua Wang, Hermann Blum, Daniel Barath, Andreas Geiger, and Marc Pollefeys. Depthsplat: Connecting gaussian splatting and depth. In Proceedings of the Computer Vision and Pattern Recognition Conference, pp.\ 16453--16463, 2025

    2025

  50. [58]

    Vr-nerf: High-fidelity virtualized walkable spaces

    Linning Xu, Vasu Agrawal, William Laney, Tony Garcia, Aayush Bansal, Changil Kim, Samuel Rota Bul \`o , Lorenzo Porzi, Peter Kontschieder, Alja z Bo z i c , et al. Vr-nerf: High-fidelity virtualized walkable spaces. In SIGGRAPH Asia 2023 Conference Papers, pp.\ 1--12, 2023

    2023

  51. [59]

    Fast3r: Towards 3d reconstruction of 1000+ images in one forward pass

    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 Proceedings of the Computer Vision and Pattern Recognition Conference, pp.\ 21...

    2025

  52. [60]

    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, pp.\ 10371--10381, 2024

    2024

  53. [61]

    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 vision and pattern recognition, pp.\ 1790--1799, 2020

    2020

  54. [62]

    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 a

    2024 arXiv

  55. [63]

    Stablenormal: Reducing diffusion variance for stable and sharp normal

    Chongjie Ye, Lingteng Qiu, Xiaodong Gu, Qi Zuo, Yushuang Wu, Zilong Dong, Liefeng Bo, Yuliang Xiu, and Xiaoguang Han. Stablenormal: Reducing diffusion variance for stable and sharp normal. ACM Transactions on Graphics (TOG), 43 0 (6): 0 1--18, 2024 b

    2024

  56. [64]

    gsplat: An open-source library for gaussian splatting

    Vickie Ye, Ruilong Li, Justin Kerr, Matias Turkulainen, Brent Yi, Zhuoyang Pan, Otto Seiskari, Jianbo Ye, Jeffrey Hu, Matthew Tancik, and Angjoo Kanazawa. gsplat: An open-source library for gaussian splatting. Journal of Machine Learning Research, 26 0 (34): 0 1--17, 2025

    2025

  57. [65]

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

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

    2023

  58. [66]

    pixelnerf: Neural radiance fields from one or few images

    Alex Yu, Vickie Ye, Matthew Tancik, and Angjoo Kanazawa. pixelnerf: Neural radiance fields from one or few images. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp.\ 4578--4587, 2021

    2021

  59. [67]

    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. arXiv preprint arXiv:2410.03825, 2024

    2024 arXiv

  60. [68]

    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 Proceedings of the Computer Vision and Pattern Recognitio...

    2025

  61. [69]

    Stereo magnification: Learning view synthesis using multiplane images

    Tinghui Zhou, Richard Tucker, John Flynn, Graham Fyffe, and Noah Snavely. Stereo magnification: Learning view synthesis using multiplane images. arXiv preprint arXiv:1805.09817, 2018

    2018 arXiv

  62. [70]

    write newline

    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 gl...

  63. [71]

    @esa (Ref

    \@ifxundefined[1] #1\@undefined \@firstoftwo \@secondoftwo \@ifnum[1] #1 \@firstoftwo \@secondoftwo \@ifx[1] #1 \@firstoftwo \@secondoftwo [2] @ #1 \@temptokena #2 #1 @ \@temptokena \@ifclassloaded agu2001 natbib The agu2001 class already includes natbib coding, so you should ...

  64. [72]

    \@lbibitem[] @bibitem@first@sw\@secondoftwo \@lbibitem[#1]#2 \@extra@b@citeb \@ifundefined br@#2\@extra@b@citeb \@namedef br@#2 \@nameuse br@#2\@extra@b@citeb \@ifundefined b@#2\@extra@b@citeb @num @parse #2 @tmp #1 NAT@b@open@#2 NAT@b@shut@#2 \@ifnum @merge>\@ne @bibitem@firs...

  65. [73]

    We report the performance of WorldMirror under different input configurations

    @open @close @open @close and [1] URL: #1 \@ifundefined chapter * \@mkboth \@ifxundefined @sectionbib * \@mkboth * \@mkboth\@gobbletwo \@ifclassloaded amsart * \@ifclassloaded amsbook * \@ifxundefined @heading @heading NAT@ctr thebibliography [1] @ \@biblabel @NAT@ctr \@bibset...

Pith tools

Reviewed August 4, 2026 · model on record in the stance chip above.