Pith. sign in

REVIEW 4 major objections 4 minor 60 references

Recollection from Pensieve: Novel View Synthesis via Learning from Uncalibrated Videos

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

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

Pith's one-line read Two-stage, fully self-supervised training can learn novel view synthesis and camera pose estimation from raw uncalibrated video frames alone, and beat methods that use camera calibration, pose, or depth supervision.

desk verdict The two-stage implicit-then-explicit training idea is genuinely new and mostly works, but the headline NVS numbers are inflated by a target-aware protocol that the paper itself admits is not true novel view synthesis. read the letter →

arxiv 2505.13440 v1 pith:NNZXABTN submitted 2025-05-19 cs.CV

Ruoyu Wang , Yi Ma , Shenghua Gao This is my paper · ORCID
classification cs.CV
keywords novelviewsynthesisuncalibratedvideocameraposeestimationself-supervisedlearning3DGaussiansplattingimplicitscenerepresentationlatentvideo-basedreconstruction
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

Almost all state-of-the-art novel view synthesis models need calibrated cameras or geometric priors during training, which shuts them out of the massive supply of raw, uncalibrated video. This paper claims that a two-stage training scheme removes that requirement. The first stage pretrains the network as an implicit scene reconstructor: it predicts per-frame latent cameras and context features and uses a view synthesis network as a proxy renderer, so the model learns cross-view correspondence without any explicit 3D representation. The second stage then predicts explicit 3D Gaussian primitives and applies Gaussian splatting rendering, depth reprojection, and smoothness losses to pull the latent representation into alignment with real 3D geometry. If the claim holds, view synthesis and pose estimation can be trained directly from raw video at scale, without preprocessing pipelines such as structure-from-motion.

What carries the argument

The load-bearing mechanism is the pairing of two training objectives that share the same predicted cameras. In Stage 1, a view synthesis transformer with Plücker-coordinate camera embeddings—each ray encoded by its direction and moment—acts as the only renderer, making every target pixel differentiable to all input pixels while the camera parameters serve as an intermediate latent code; the strict-subset context rule prevents the code from degenerating into a frame index lookup. In Stage 2, the same cameras are used to back-project predicted depth into 3D Gaussian primitives, and the Gaussian splatting rendering loss plus depth projection and smoothness losses force the latent code to respect real 3D geometry. The essential identity is that the cameras must satisfy both the implicit rendering objective and the explicit geometric rendering objective simultaneously, which is what transfers the latent representation into a physically consistent world frame.

What would settle it

Train the same pipeline on a set of videos with large, varied rotations and translations, and compare the predicted relative poses against a reliable structure-from-motion reconstruction on every scene; if the photometric losses stay low while pose errors grow large on the high-rotation scenes, the Stage 2 losses have found a self-consistent but geometrically wrong solution, and the pose accuracy reported on RealEstate10K would be inflated by its many videos with only small rotations.

Watch

Extended reading notes

Core claim

On the paper's own terms, the discovery is that implicit latent reconstruction pretraining and explicit geometric alignment are complementary, and together they let a single network produce both novel views and camera poses from uncalibrated video. In Stage 1 the model treats the predicted per-frame camera as a latent code with the right number of degrees of freedom to encode the target image; the context frames are always a strict subset of the input frames so the network cannot trivially copy the target. In Stage 2 the model predicts pixel-aligned 2D Gaussian primitives, with centers obtained by back-projecting predicted depth through the predicted cameras, renders them with Gaussian splatting, and adds depth projection and edge-aware smoothness losses alongside the Stage 1 rendering loss. The authors report that on RealEstate10K this self-supervised model reaches 26.53 dB PSNR for target-aware novel view synthesis, surpassing baselines trained with intrinsics, poses, depth, or matching, and that on DL3DV-140 it also leads in pose accuracy; ablations show that without Stage 1 training fails to converge, and without Stage 2 the latent cameras are not anchored to real space.

Load-bearing premise

The load-bearing premise is that the self-supervised photometric losses in Stage 2—Gaussian splatting rendering plus depth reprojection and smoothness—are strong enough to force the predicted cameras and geometry to agree with the true shape of the scene, even though no real-world scale, orientation, or camera calibration is ever observed.

Editorial extensions

If this is right

  • Training view synthesis models directly on raw video removes the structure-from-motion or SLAM preprocessing step, which is the main bottleneck for scaling 3D vision models to diverse, unconstrained web video.
  • Because pose estimates emerge from the same network without pose supervision, the method offers a way to obtain relative camera trajectories from video alone, useful for downstream reconstruction, navigation, and video editing.
  • The ablations imply that the implicit pretraining stage is not an optional warm-up: removing it makes the explicit Gaussian alignment fail to converge, so latent correspondence learning is a necessary precondition for self-supervised geometric alignment.
  • The interpolated-frame inference scheme shows that two-view inputs are a degeneracy point for the alignment; robustness improves whenever context overlap is increased, suggesting a direct trade-off between input frame spacing and reconstruction quality.
  • Enforcing explicit 3D consistency in Stage 2 lowers image quality slightly relative to pure latent reconstruction, so applications that prioritize appearance over geometry would make a different trade-off than applications that need geometrically consistent scenes.

Reading between the lines

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

  • If the result transfers to longer, more diverse videos, it suggests the field's reliance on precomputed calibration is partly a habit rather than a hard requirement; uncalibrated training could become the default for large-scale 3D foundation models.
  • The static-scene limitation points to a concrete extension: replace the rigid world assumption with per-frame latent motion or scene flow in Stage 1, then align a dynamic Gaussian representation in Stage 2.
  • A reader could probe how much geometry the implicit stage already learns by inspecting the latent cameras before Stage 2; the ablations show rotations and translations already trend toward ground truth, which is more geometric content than the paper's framing claims.
  • The depth reprojection loss in Stage 2 samples a single neighbor frame per projection; using multiple concurrent projection targets might lock down poses more tightly on long videos and would be a direct testable modification of the loss.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 4 minor

Summary. The paper proposes a two-stage training strategy for novel view synthesis (NVS) models trained from uncalibrated video frames or multi-view images, without camera parameters, depth, or other geometric priors. In Stage 1, the model performs implicit latent reconstruction: it predicts per-frame latent cameras and context features and uses an LVSM-style view synthesis transformer to re-render input frames, supervised by MSE and LPIPS losses. In Stage 2, the pretrained model additionally predicts pixel-aligned 2D Gaussian primitives and is trained with a 3D Gaussian Splatting rendering loss and depth projection/smoothness losses to align the latent representation with physical 3D geometry. The paper also proposes an inference-time interpolated-frame scheme for the two-input-frame case. Experiments on RealEstate10K and DL3DV-140 report state-of-the-art NVS quality and competitive camera pose estimation compared with methods that use calibration, pose, or depth supervision. Ablations show that Stage 1 pretraining is necessary for convergence and that the two stages are complementary.

Significance. If the claims hold, the contribution is significant: it demonstrates a practical path toward training generalizable NVS and pose-estimation models on large-scale raw video without SfM preprocessing, and the two-stage design is a reasonable response to the optimization difficulties of fully self-supervised explicit 3D reconstruction. The paper ships code, and the ablation evidence that Stage 1 is necessary is concrete and internally consistent. However, the empirical support for the headline claim is weakened by the evaluation protocol: the leading tables use a target-aware protocol that the authors themselves acknowledge is not true novel view synthesis, and the pose metrics are partly insensitive on the chosen datasets. The central idea is defensible, but the paper needs a substantially restructured evaluation before the claims can be accepted as stated.

major comments (4)
  1. [Sec. 4.3, Tables 1-2, Sec. 4.4, Abstract] The headline NVS claim is based on the target-aware evaluation, which the paper itself states 'actually encodes and decodes the target view, rather than performing true novel view synthesis' (Sec. 4.3). Nevertheless, Tables 1 and 2 lead with target-aware numbers and Sec. 4.4 concludes from them that the method 'achieves the best novel view synthesis quality.' The valid target-aligned evidence is much weaker: on RealEstate10K, Ours reaches 22.20 PSNR versus PF3plat's 20.04, and the improved 23.96 PSNR (Ours*) requires 40 iterations of test-view pose optimization with the Gaussian Splatting rendering loss. The abstract and conclusion should be rephrased so that the true-NVS claim rests on the target-aligned results, and the target-aware protocol should be presented as a diagnostic or as a separate 'target-encoding' setting, not as the primary NVS comparison.
  2. [Sec. 4.5, Table 4] The pose accuracy evidence is partly undermined by metric insensitivity. The paper notes that RealEstate10K contains many scenes with minimal camera rotation and that the untrained model already achieves RRA@5 = 36.6, the same value as the failed 'w/o Stage 1' model. Given this, RRA@5 alone cannot support the claim of 'accurate camera pose estimation.' The authors should report additional pose metrics such as median RRA/RTA, RRA@1, or results on a subset of scenes with large rotation and translation, and they should provide error bars or multiple-seed results, since all tables appear to report a single run.
  3. [Sec. 3.4, Eqs. (6)-(7), Sec. 4.1] The self-supervised Stage 2 losses do not observe metric scale or absolute orientation, so the model could in principle converge to a self-consistent but physically incorrect solution. The paper does not explicitly test this gauge ambiguity, and the datasets used (RealEstate10K and DL3DV) are dominated by relatively constrained camera motions. To substantiate the pose-estimation claim, the authors should evaluate on sequences with arbitrary or wide-baseline motions, or otherwise demonstrate that the predicted cameras align with ground truth beyond the near-identity-rotation regime where RRA@5 is already inflated.
  4. [Sec. 4.5, Tables 3-4] The contribution of Stage 2 to true novel view synthesis quality is not isolated under the valid protocol. Table 3 (target-aligned) includes 'w/o Stage 1' and 'w/o IF' but no 'w/o Stage 2'; Table 4 includes 'w/o Stage 2' only under the target-aware protocol, which is not a measure of NVS. Since the paper argues that the two stages are complementary and that Stage 2 'enforces 3D consistency,' the target-aligned effect of removing Stage 2 should be reported to support that specific claim.
minor comments (4)
  1. [Sec. 4.3] There is a typo in 'this acutally encodes and decodes the target view'—should be 'actually.'
  2. [Tables 1-2] The captions read 'Qualitative Comparison,' but the tables contain quantitative metrics; the captions should say 'Quantitative Comparison.'
  3. [Sec. 3.4, Eq. (6)] The projection loss is described for an i-th frame projected onto a j-th frame, but the notation does not specify how the loss is aggregated over i and j; please clarify the summation and the random sampling of j.
  4. [Sec. 3.5] The interpolated-frame scheme is introduced as an inference-time strategy, but it is unclear whether it is applied for all two-frame test cases and whether the comparison methods receive any analogous treatment; please state this explicitly.

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity: the two-stage self-supervised training is not fitted to evaluation targets, and the acknowledged target-aware protocol is an evaluation caveat rather than a circular derivation.

full rationale

The derivation chain is self-contained. Stage 1 trains camera, context, and view-synthesis networks against the input frames themselves (Eq. 4), and Stage 2 adds explicit 3D Gaussian Splatting rendering and depth-projection losses (Eqs. 5-7); neither stage uses held-out evaluation poses or images as training supervision. No parameter is fitted to the evaluation targets and then renamed a prediction. The paper's self-citations (PlaneDepth [48], 2DGS [15]) are to component choices and are not load-bearing, and no uniqueness theorem from the authors is invoked. The paper itself flags a genuine limitation in Sec. 4.3, Target-aware Evaluation: 'this actually encodes and decodes the target view, rather than performing true novel view synthesis.' This weakens the headline PSNR numbers, and the target-aligned results plus the 40-iteration test-pose optimization should be read as the cleaner evidence. That is an evaluation-protocol concern, not circularity: the predicted image is not by construction equal to the target image, and the target-aligned block provides an independent check. Overall, no step reduces to its own input; the central claim has independent empirical content.

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

No new physical entities are introduced; the latent camera parameters are an internal network representation rather than a hypothesized world component.

free parameters (4)
  • lambda_1 = not reported (annealed to 0)
    Weight balancing projection loss in Eq. (7); no value given in the paper.
  • lambda_2 = not reported
    Weight balancing depth smoothness loss in Eq. (7).
  • w_low = 0.1
    Lower weight assigned to context-frame reconstruction loss in Eq. (4) to avoid trivial index copying.
  • gamma = not reported
    Edge-aware smoothness exponent in Eq. (6).
assumptions (4)
  • domain assumption The scene is static.
    Explicitly stated in the Limitations section; dynamic scenes are out of scope.
  • domain assumption All frames in a video share the same intrinsic camera parameters and the principal point is at the image center.
    Section 3.1 postulates this pinhole camera model to reduce the number of unknowns.
  • domain assumption An LVSM-style view synthesis transformer can serve as a proxy renderer whose output is differentiable with respect to all input pixels.
    Stage 1 pretraining relies on this property of LVSM [19] to learn correspondences implicitly.
  • domain assumption Gaussian Splatting and depth reprojection provide meaningful gradient signals for camera and geometry alignment.
    Stage 2 uses these losses as the only geometric supervision; the paper does not prove they lack degenerate local minima.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Recollection from Pensieve: Novel View Synthesis via Learning from Uncalibrated Videos." pith.science (2026). https://pith.science/paper/NNZXABTN

@misc{pith2026250513440,
  author       = {Pith},
  title        = {Pith review of: Recollection from Pensieve: Novel View Synthesis via Learning from Uncalibrated Videos},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/NNZXABTN}},
  note         = {Machine review of arXiv:2505.13440}
}
read the original abstract

Currently almost all state-of-the-art novel view synthesis and reconstruction models rely on calibrated cameras or additional geometric priors for training. These prerequisites significantly limit their applicability to massive uncalibrated data. To alleviate this requirement and unlock the potential for self-supervised training on large-scale uncalibrated videos, we propose a novel two-stage strategy to train a view synthesis model from only raw video frames or multi-view images, without providing camera parameters or other priors. In the first stage, we learn to reconstruct the scene implicitly in a latent space without relying on any explicit 3D representation. Specifically, we predict per-frame latent camera and scene context features, and employ a view synthesis model as a proxy for explicit rendering. This pretraining stage substantially reduces the optimization complexity and encourages the network to learn the underlying 3D consistency in a self-supervised manner. The learned latent camera and implicit scene representation have a large gap compared with the real 3D world. To reduce this gap, we introduce the second stage training by explicitly predicting 3D Gaussian primitives. We additionally apply explicit Gaussian Splatting rendering loss and depth projection loss to align the learned latent representations with physically grounded 3D geometry. In this way, Stage 1 provides a strong initialization and Stage 2 enforces 3D consistency - the two stages are complementary and mutually beneficial. Extensive experiments demonstrate the effectiveness of our approach, achieving high-quality novel view synthesis and accurate camera pose estimation, compared to methods that employ supervision with calibration, pose, or depth information. The code is available at https://github.com/Dwawayu/Pensieve.

Figures

Figures reproduced from arXiv: 2505.13440 by the authors.

Figure 1
Figure 1. Pipeline. Given an N-frame uncalibrated video (with N = 4 shown), our method first extracts per-frame features F s using a shared-weight Per-Frame Transformer. Features from all frames are passed through a Camera Transformer to predict per-frame camera parameters K and P. A sampled proper subset of features (e.g., {F s 2 , F s 4}) is fed into the Context Transformer to predict the corresponding context features F c … view at source ↗
Figure 2
Figure 2. Novel view synthesis comparisons on RealEstate10K [ [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 4
Figure 4. Comparison of the camera trajectories with and [PITH_FULL_IMAGE:figures/full_fig_p009_4.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

60 extracted references · 26 canonical work pages

  1. [1]

    Building rome in a day

    Sameer Agarwal, Yasutaka Furukawa, Noah Snavely, Ian Simon, Brian Curless, Steven M Seitz, and Richard Szeliski. Building rome in a day. Communications of the ACM, 54(10):105–112, 2011

    2011

  2. [2]

    Mip-nerf: A multiscale representation for anti-aliasing neural radiance fields

    Jonathan T Barron, Ben Mildenhall, Matthew Tancik, Peter Hedman, Ricardo Martin-Brualla, and Pratul P Srinivasan. Mip-nerf: A multiscale representation for anti-aliasing neural radiance fields. In Proceedings of the IEEE/CVF international conference on computer vision , pages 5855–5864, 2021

    work page 2021

  3. [3]

    Nope-nerf: Optimising neural radiance field with no pose prior

    Wenjing Bian, Zirui Wang, Kejie Li, Jia-Wang Bian, and Victor Adrian Prisacariu. Nope-nerf: Optimising neural radiance field with no pose prior. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4160–4169, 2023

    work page 2023

  4. [4]

    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 , pages 19457–19467, 2024

    work page 2024

  5. [5]

    Mvsnerf: Fast generalizable radiance field reconstruction from multi-view stereo

    Anpei Chen, Zexiang Xu, Fuqiang Zhao, Xiaoshuai Zhang, Fanbo Xiang, Jingyi Yu, and Hao Su. Mvsnerf: Fast generalizable radiance field reconstruction from multi-view stereo. In Proceedings of the IEEE/CVF international conference on computer vision , pages 14124–14133, 2021

    2021

  6. [6]

    Tensorf: Tensorial radiance fields

    Anpei Chen, Zexiang Xu, Andreas Geiger, Jingyi Yu, and Hao Su. Tensorf: Tensorial radiance fields. In European conference on computer vision, pages 333–350. Springer, 2022

    2022

  7. [7]

    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

  8. [8]

    K-planes: Explicit radiance fields in space, time, and appearance

    Sara Fridovich-Keil, Giacomo Meanti, Frederik Rahbæk Warburg, Benjamin Recht, and Angjoo Kanazawa. K-planes: Explicit radiance fields in space, time, and appearance. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12479–12488, 2023

    2023

Show all 60 references
  1. [9]

    Colmap-free 3d gaussian splatting

    Yang Fu, Sifei Liu, Amey Kulkarni, Jan Kautz, Alexei A Efros, and Xiaolong Wang. Colmap-free 3d gaussian splatting. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 20796–20805, 2024

    2024

  2. [10]

    Unsupervised monocular depth estimation with left-right consistency

    Clément Godard, Oisin Mac Aodha, and Gabriel J Brostow. Unsupervised monocular depth estimation with left-right consistency. In Proceedings of the IEEE conference on computer vision and pattern recognition , pages 270–279, 2017

    2017

  3. [11]

    Digging into self-supervised monocular depth estimation

    Clément Godard, Oisin Mac Aodha, Michael Firman, and Gabriel J Brostow. Digging into self-supervised monocular depth estimation. In Proceedings of the IEEE/CVF international conference on computer vision , pages 3828–3838, 2019

    2019

  4. [12]

    Forget about the lidar: Self-supervised depth estimators with med probability volumes

    Juan Luis GonzalezBello and Munchurl Kim. Forget about the lidar: Self-supervised depth estimators with med probability volumes. Advances in Neural Information Processing Systems , 33:12626–12637, 2020

    2020

  5. [13]

    Pf3plat: Pose-free feed-forward 3d gaussian splatting

    Sunghwan Hong, Jaewoo Jung, Heeseong Shin, Jisang Han, Jiaolong Yang, Chong Luo, and Seungryong Kim. Pf3plat: Pose-free feed-forward 3d gaussian splatting. arXiv preprint arXiv:2410.22128, 2024

    2024 arXiv

  6. [14]

    Unifying correspondence pose and nerf for generalized pose-free novel view synthesis

    Sunghwan Hong, Jaewoo Jung, Heeseong Shin, Jiaolong Yang, Seungryong Kim, and Chong Luo. Unifying correspondence pose and nerf for generalized pose-free novel view synthesis. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 20196–20206, 2024

    2024

  7. [15]

    2d gaussian splatting for geometrically accurate radiance fields

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

    2024

  8. [16]

    Sc-gs: Sparse- controlled gaussian splatting for editable dynamic scenes

    Yi-Hua Huang, Yang-Tian Sun, Ziyi Yang, Xiaoyang Lyu, Yan-Pei Cao, and Xiaojuan Qi. Sc-gs: Sparse- controlled gaussian splatting for editable dynamic scenes. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages 4220–4230, 2024

    2024

  9. [17]

    Self-calibrating neural radiance fields

    Yoonwoo Jeong, Seokjun Ahn, Christopher Choy, Anima Anandkumar, Minsu Cho, and Jaesik Park. Self-calibrating neural radiance fields. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 5846–5854, 2021

    2021

  10. [18]

    A construct-optimize approach to sparse view synthesis without camera pose

    Kaiwen Jiang, Yang Fu, Mukund Varma T, Yash Belhe, Xiaolong Wang, Hao Su, and Ravi Ramamoorthi. A construct-optimize approach to sparse view synthesis without camera pose. In ACM SIGGRAPH 2024 Conference Papers, pages 1–11, 2024. 10

    2024

  11. [19]

    Lvsm: A large view synthesis model with minimal 3d inductive bias

    Haian Jin, Hanwen Jiang, Hao Tan, Kai Zhang, Sai Bi, Tianyuan Zhang, Fujun Luan, Noah Snavely, and Zexiang Xu. Lvsm: A large view synthesis model with minimal 3d inductive bias. In The Thirteenth International Conference on Learning Representations , 2025

    2025

  12. [20]

    Selfsplat: Pose-free and 3d prior-free generalizable 3d gaussian splatting

    Gyeongjin Kang, Jisang Yoo, Jihyeon Park, Seungtae Nam, Hyeonsoo Im, Sangheon Shin, Sangpil Kim, and Eunbyung Park. Selfsplat: Pose-free and 3d prior-free generalizable 3d gaussian splatting. arXiv preprint arXiv:2411.17190, 2024

    2024 arXiv

  13. [21]

    3d gaussian splatting for real-time radiance field rendering

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

    2023

  14. [22]

    Dense visual slam for rgb-d cameras

    Christian Kerl, Jürgen Sturm, and Daniel Cremers. Dense visual slam for rgb-d cameras. In2013 IEEE/RSJ international conference on intelligent robots and systems , pages 2100–2106. IEEE, 2013

    2013

  15. [23]

    Grounding image matching in 3d with mast3r

    Vincent Leroy, Yohann Cabon, and Jérôme Revaud. Grounding image matching in 3d with mast3r. In European Conference on Computer Vision, pages 71–91. Springer, 2024

    2024

  16. [24]

    Ggrt: Towards pose-free generalizable 3d gaussian splatting in real-time

    Hao Li, Yuanyuan Gao, Chenming Wu, Dingwen Zhang, Yalun Dai, Chen Zhao, Haocheng Feng, Errui Ding, Jingdong Wang, and Junwei Han. Ggrt: Towards pose-free generalizable 3d gaussian splatting in real-time. In European Conference on Computer Vision, pages 325–341. Springer, 2024

    2024

  17. [25]

    Mine: Towards continuous depth mpi with nerf for novel view synthesis

    Jiaxin Li, Zijian Feng, Qi She, Henghui Ding, Changhu Wang, and Gim Hee Lee. Mine: Towards continuous depth mpi with nerf for novel view synthesis. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 12578–12588, 2021

    2021

  18. [26]

    Neural scene flow fields for space-time view synthesis of dynamic scenes

    Zhengqi Li, Simon Niklaus, Noah Snavely, and Oliver Wang. Neural scene flow fields for space-time view synthesis of dynamic scenes. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6498–6508, 2021

    2021

  19. [27]

    Neuralangelo: High-fidelity neural surface reconstruction

    Zhaoshuo Li, Thomas Müller, Alex Evans, Russell H Taylor, Mathias Unberath, Ming-Yu Liu, and Chen- Hsuan Lin. Neuralangelo: High-fidelity neural surface reconstruction. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8456–8465, 2023

    2023

  20. [28]

    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

  21. [29]

    Barf: Bundle-adjusting neural radiance fields

    Chen-Hsuan Lin, Wei-Chiu Ma, Antonio Torralba, and Simon Lucey. Barf: Bundle-adjusting neural radiance fields. In Proceedings of the IEEE/CVF international conference on computer vision , pages 5741–5751, 2021

    2021

  22. [30]

    Lightglue: Local feature matching at light speed

    Philipp Lindenberger, Paul-Edouard Sarlin, and Marc Pollefeys. Lightglue: Local feature matching at light speed. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 17627–17638, 2023

    2023

  23. [31]

    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 , p...

    2024

  24. [32]

    Scaffold-gs: Structured 3d gaussians for view-adaptive rendering

    Tao Lu, Mulin Yu, Linning Xu, Yuanbo Xiangli, Limin Wang, Dahua Lin, and Bo Dai. Scaffold-gs: Structured 3d gaussians for view-adaptive rendering. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 20654–20664, 2024

    2024

  25. [33]

    Gaussian splatting slam

    Hidenobu Matsuki, Riku Murai, Paul HJ Kelly, and Andrew J Davison. Gaussian splatting slam. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 18039– 18048, 2024

    2024

  26. [34]

    Progressively optimized local radiance fields for robust view synthesis

    Andreas Meuleman, Yu-Lun Liu, Chen Gao, Jia-Bin Huang, Changil Kim, Min H Kim, and Johannes Kopf. Progressively optimized local radiance fields for robust view synthesis. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16539–16548, 2023

    2023

  27. [35]

    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 (1):99–106, 2021

    2021

  28. [36]

    Instant neural graphics primitives with a multiresolution hash encoding

    Thomas Müller, Alex Evans, Christoph Schied, and Alexander Keller. Instant neural graphics primitives with a multiresolution hash encoding. ACM transactions on graphics (TOG), 41(4):1–15, 2022. 11

    2022

  29. [37]

    Splinegs: Robust motion-adaptive spline for real-time dynamic 3d gaussians from monocular video

    Jongmin Park, Minh-Quan Viet Bui, Juan Luis Gonzalez Bello, Jaeho Moon, Jihyong Oh, and Munchurl Kim. Splinegs: Robust motion-adaptive spline for real-time dynamic 3d gaussians from monocular video. arXiv preprint arXiv:2412.09982, 2024

    2024 arXiv

  30. [38]

    Nerfies: Deformable neural radiance fields

    Keunhong Park, Utkarsh Sinha, Jonathan T Barron, Sofien Bouaziz, Dan B Goldman, Steven M Seitz, and Ricardo Martin-Brualla. Nerfies: Deformable neural radiance fields. In Proceedings of the IEEE/CVF international conference on computer vision , pages 5865–5874, 2021

    2021

  31. [39]

    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

  32. [40]

    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

  33. [41]

    Flowcam: Training generalizable 3d radiance fields without camera poses via pixel-aligned scene flow

    Cameron Smith, Yilun Du, Ayush Tewari, and Vincent Sitzmann. Flowcam: Training generalizable 3d radiance fields without camera poses via pixel-aligned scene flow. arXiv preprint arXiv:2306.00180, 2023

    2023 arXiv

  34. [42]

    Light field neural rendering

    Mohammed Suhail, Carlos Esteves, Leonid Sigal, and Ameesh Makadia. Light field neural rendering. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 8269–8279, 2022

    2022

  35. [43]

    Splatter image: Ultra-fast single-view 3d reconstruction

    Stanislaw Szymanowicz, Chrisitian Rupprecht, and Andrea Vedaldi. Splatter image: Ultra-fast single-view 3d reconstruction. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages 10208–10217, 2024

    2024

  36. [44]

    Lgm: Large multi-view gaussian model for high-resolution 3d content creation

    Jiaxiang Tang, Zhaoxi Chen, Xiaokang Chen, Tengfei Wang, Gang Zeng, and Ziwei Liu. Lgm: Large multi-view gaussian model for high-resolution 3d content creation. In European Conference on Computer Vision, pages 1–18. Springer, 2024

    2024

  37. [45]

    Mononerf: Learning a generalizable dynamic radiance field from monocular videos

    Fengrui Tian, Shaoyi Du, and Yueqi Duan. Mononerf: Learning a generalizable dynamic radiance field from monocular videos. In Proceedings of the IEEE/CVF International Conference on Computer Vision , pages 17903–17913, 2023

    2023

  38. [46]

    Sparf: Neural radiance fields from sparse and noisy poses

    Prune Truong, Marie-Julie Rakotosaona, Fabian Manhardt, and Federico Tombari. Sparf: Neural radiance fields from sparse and noisy poses. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4190–4200, 2023

    2023

  39. [47]

    Ibrnet: Learning multi-view image-based rendering

    Qianqian Wang, Zhicheng Wang, Kyle Genova, Pratul P Srinivasan, Howard Zhou, Jonathan T Barron, Ricardo Martin-Brualla, Noah Snavely, and Thomas Funkhouser. Ibrnet: Learning multi-view image-based rendering. In Proceedings of the IEEE/CVF conference on computer vision and patt...

    2021

  40. [48]

    Planedepth: Self-supervised depth estimation via orthogonal planes

    Ruoyu Wang, Zehao Yu, and Shenghua Gao. Planedepth: Self-supervised depth estimation via orthogonal planes. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 21425–21434, 2023

    2023

  41. [49]

    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, pages 20697–20709, 2024

    2024

  42. [50]

    Nerf–: Neural radiance fields without known camera parameters

    Zirui Wang, Shangzhe Wu, Weidi Xie, Min Chen, and Victor Adrian Prisacariu. Nerf–: Neural radiance fields without known camera parameters. arXiv preprint arXiv:2102.07064, 2021

    2021 arXiv

  43. [51]

    Grm: Large gaussian reconstruction model for efficient 3d reconstruction and generation

    Yinghao Xu, Zifan Shi, Wang Yifan, Hansheng Chen, Ceyuan Yang, Sida Peng, Yujun Shen, and Gordon Wetzstein. Grm: Large gaussian reconstruction model for efficient 3d reconstruction and generation. In European Conference on Computer Vision, pages 1–20. Springer, 2024

    2024

  44. [52]

    Spectrally pruned gaussian fields with neural compensation

    Runyi Yang, Zhenxin Zhu, Zhou Jiang, Baijun Ye, Xiaoxue Chen, Yifei Zhang, Yuantao Chen, Jian Zhao, and Hao Zhao. Spectrally pruned gaussian fields with neural compensation. arXiv preprint arXiv:2405.00676, 2024

    2024 arXiv

  45. [53]

    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

  46. [54]

    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 , pages 4578–4587, 2021. 12

    2021

  47. [55]

    Cogs: Controllable gaussian splatting

    Heng Yu, Joel Julin, Zoltán Á Milacski, Koichiro Niinuma, and László A Jeni. Cogs: Controllable gaussian splatting. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 21624–21633, 2024

    2024

  48. [56]

    Mip-splatting: Alias-free 3d gaussian splatting

    Zehao Yu, Anpei Chen, Binbin Huang, Torsten Sattler, and Andreas Geiger. Mip-splatting: Alias-free 3d gaussian splatting. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 19447–19456, 2024

    2024

  49. [57]

    Gs-lrm: Large reconstruction model for 3d gaussian splatting

    Kai Zhang, Sai Bi, Hao Tan, Yuanbo Xiangli, Nanxuan Zhao, Kalyan Sunkavalli, and Zexiang Xu. Gs-lrm: Large reconstruction model for 3d gaussian splatting. In European Conference on Computer Vision, pages 1–19. Springer, 2024

    2024

  50. [58]

    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. arXiv preprint arXiv:2502.12138, 2025

    2025

  51. [59]

    Unsupervised learning of depth and ego-motion from video

    Tinghui Zhou, Matthew Brown, Noah Snavely, and David G Lowe. Unsupervised learning of depth and ego-motion from video. In Proceedings of the IEEE conference on computer vision and pattern recognition , pages 1851–1858, 2017

    2017

  52. [60]

    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. ACM Trans. Graph. (Proc. SIGGRAPH), 37, 2018. 13

    2018

Pith tools

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