Pith. sign in

REVIEW 1 major objections 3 minor 64 references

Disentangled Geometry and Appearance for Efficient Multi-View Surface Reconstruction and Rendering

T0 review · 1 major / 3 minor · reviewed 2026-08-05 · deepseek-v4-flash

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

Pith's one-line read Multi-view surface reconstruction can be done in about five minutes on an explicit mesh, producing an editable mesh directly without a separate extraction step.

desk verdict Can't judge the work from the supplied text—the body is mojibake—but the abstract makes a concrete, plausible claim that deserves referee time if the actual PDF is readable. read the letter →

arxiv 2508.17436 v1 pith:I5FVKGR3 submitted 2025-08-24 cs.CV

Qitong Zhang , Jieqing Feng This is my paper
classification cs.CV
keywords multi-viewreconstructionexplicitmeshdifferentiablerasterizationneuralshaderdeformationfieldsurfaceeditablerenderingspeed
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 sets out to show that high-quality multi-view 3D reconstruction does not require volumetric neural rendering followed by mesh extraction. It proposes an explicit-mesh pipeline with a disentangled geometry and appearance model: geometry is optimized directly on the mesh, while appearance is handled by a neural shader whose geometric input is regularized, plus a view-invariant diffuse term baked into vertices. If correct, the method delivers training in 4.84 minutes and rendering at 0.023 seconds per frame, with reconstruction quality competitive with top-performing methods, and outputs an editable mesh and texture as a by-product. The practical payoff is that downstream applications—editing, relighting, real-time rendering—can use the output immediately.

What carries the argument

The load-bearing object is the explicit triangle mesh itself, treated as a trainable parameter and rendered by differentiable rasterization instead of volumetric raymarching. Around it, the method builds a neural deformation field that gives each vertex access to global scene context, and a tailored regularization that constrains the geometric features handed to the neural shader. These three pieces together replace the usual implicit volume plus mesh extraction pipeline, and the baked view-invariant diffuse term is what makes the per-frame rendering cost low.

What would settle it

Take a sparse-view capture (e.g., three to five views) of an object with concavities or thin structures and train the method; if surface completeness drops sharply or the mesh develops holes compared with dense-view runs, the geometry-context assumption is the failure point. A second check: edit or relight the baked diffuse texture and see whether shading artifacts reveal that view-dependent appearance leaked into the baked term.

Watch

Extended reading notes

Core claim

The central claim is that volumetric reconstruction is unnecessary: an explicit triangle mesh, optimized through a differentiable rasterizer, can reach competitive surface quality while training in minutes and rendering at 23 milliseconds per frame. To make this work, the method separates geometry from appearance. A neural deformation field supplies global geometric context for the mesh vertices, and a regularization term keeps the geometric features fed to the neural shader faithful, so shading does not drift into geometry corrections. A view-invariant diffuse component is baked into mesh vertices, cutting per-frame shading cost. The result is a mesh directly—no marching cubes or extraction

Load-bearing premise

The method assumes that its neural deformation field, trained with the proposed regularization, gives the explicit mesh enough global context to converge to a good surface from color and silhouette losses alone—without volumetric rendering, depth maps, or dense view coverage. If that context is insufficient for complex topology or sparse views, the speed-quality trade-off unravels.

Editorial extensions

If this is right

  • Because the mesh is optimized directly, the method skips marching cubes; the reconstructed surface is ready for editing, texturing, or animation without an extraction or post-processing step.
  • Training completes in 4.84 minutes on standard multi-view datasets, putting per-scene reconstruction in a range where iterative refinement during capture becomes practical.
  • Rendering at 0.023 seconds per frame means the reconstructed model can be displayed and manipulated at interactive rates on commodity hardware.
  • Baking a view-invariant diffuse term into mesh vertices removes per-frame view-dependent shading for the diffuse component, which is where much of the rendering speedup comes from.
  • The disentanglement of geometry from appearance means the same geometry can be re-rendered with different appearance models, which is what makes texture and mesh editing natural.

Reading between the lines

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

  • Editorial: The same explicit-mesh-plus-deformation recipe could extend to dynamic scenes by making the deformation field time-dependent; the paper does not claim this, but nothing in the design blocks it.
  • Editorial: Because the diffuse appearance is baked into vertices, relighting under new illumination would likely require a separate reflectance model; the current pipeline is best understood as fixed-lighting, not relightable.
  • Editorial: If the speed holds at higher mesh resolutions, the approach could be embedded in real-time scanning pipelines that currently trade quality for speed.
  • Editorial: A direct test of the disentanglement claim would be to swap the neural shader for a simple analytic shader after training and see whether geometry still renders correctly—if it does, the 'disentangled' description is confirmed.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

1 major / 3 minor

Summary. The paper proposes a multi-view surface reconstruction and rendering method based on explicit mesh representation with differentiable rasterization. It introduces a disentangled geometry/appearance model, a neural deformation field for global geometric context, a geometric-feature regularizer for the neural shader, and a view-invariant diffuse term baked into mesh vertices. The authors claim state-of-the-art training speed (4.84 minutes), fast rendering (0.023 seconds), competitive reconstruction quality, and direct output of editable meshes without a separate extraction step. The supplied full text, however, is almost entirely corrupted/mojibake: equations, algorithm details, tables, ablations, and the limitations paragraph are unreadable. As a result, the technical content cannot be independently verified from the manuscript as provided.

Significance. If the claims hold, the contribution is potentially significant: an efficient multi-view surface reconstruction method that directly outputs an editable mesh and achieves competitive quality at roughly 4.84 minutes of training and 0.023 seconds per rendered frame would be practically valuable, especially for downstream mesh editing and real-time rendering. The general direction—explicit mesh plus differentiable rasterization with small neural components—is plausible and timely. The paper also makes a falsifiable speed/quality claim that could be checked on standard benchmarks. However, the current manuscript provides no recoverable equations, no legible tables, no implementation details, and no reproducibility artifacts (code or checkpoints). Therefore the significance remains an assertion rather than an assessable result.

major comments (1)
  1. [Full text (Section 3 and Tables 1–3)] The supplied manuscript is unreadable: essentially all equations, algorithm descriptions, ablation text, and table entries are corrupted replacement characters. I cannot identify the deformation-field objective, the geometric-feature regularizer, the shader architecture, the training schedule, the dataset, or the hardware used for the claimed 4.84-minute training and 0.023-second rendering times. The load-bearing assumption—that the deformation field supplies sufficient global context so that the explicit mesh and neural shader converge to a high-quality surface without volumetric rendering or depth supervision—is therefore uncheckable. This is not a demonstrated technical error, but it prevents verification of the central claim and makes the paper unreviewable in its current form.
minor comments (3)
  1. [Abstract and Section 1] The abstract states the model "does not rely on deep networks," yet the method includes a "neural deformation field" and a "neural shader." If these are networks (even small ones), the claim is misleading and should be clarified to say that the core geometry/appearance representation is network-free. This matters for the efficiency argument because the training cost of these neural components is part of the reported 4.84 minutes.
  2. [Tables and experiments] The quantitative tables appear as unlabeled rows of digits; metric names, dataset names, baseline names, and error bars (if any) are not recoverable. The claim of "state-of-the-art" speed and "competitive" quality cannot be checked, and the rendering time of 0.023 seconds is not accompanied by a readable specification of image resolution or hardware.
  3. [Limitations (final paragraphs)] The passage that likely states limitations and future work is also garbled. Per the reviewing rules I flag this explicitly: the authors' own caveats regarding failure modes on complex topology, sparse views, or other conditions are not recoverable, so even the self-acknowledged boundaries of the method cannot be assessed.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity demonstrated: the full text is unreadable mojibake, and no quoted reduction to the paper's own inputs can be exhibited.

full rationale

The supplied full text of arXiv:2508.17436 is almost entirely encoding-corrupted; equations, ablations, and implementation details are not recoverable. Under the hard rule that circularity may only be claimed with a specific quote and exhibited reduction (e.g., Eq. X = Eq. Y by construction, or a fitted parameter renamed as a prediction), no such step can be identified. The abstract's efficiency and quality claims are benchmarked externally (training time, rendering time, reconstruction quality against top-performing methods), which provides independent grounding rather than a self-referential derivation. The central methodological choices—an explicit mesh rasterizer, a neural deformation field, and a geometric-feature regularizer—are not shown in the available text to be defined in terms of the quantities they are said to predict. The fact that the text is unreadable is an evidentiary limitation, not evidence of circularity. Accordingly, the honest finding is no significant circularity, score 0.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

Because only the abstract is available, the ledger is necessarily incomplete. We identified the most salient assumptions inferred from the abstract; many specific choices (e.g., loss weights, per-scene scales) cannot be enumerated without the full text.

assumptions (3)
  • domain assumption Input multi-view images are calibrated with known camera poses.
    Multi-view reconstruction generally assumes known camera parameters; the abstract does not state this explicitly, but it is implied by the problem setup.
  • domain assumption The differentiable rasterizer provides gradients that are sufficient to optimize explicit mesh geometry and vertex attributes.
    The method relies on differentiable rasterization to propagate image losses to mesh vertices; this is a standard but unproven assumption about gradient quality.
  • ad hoc to paper The proposed regularization on geometric features preserves shading accuracy without distorting geometry.
    The abstract claims the regularization constrains geometric features to boost shading accuracy; if this design choice does not hold, the method's quality claims could fail. No proof is recoverable from the abstract.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Disentangled Geometry and Appearance for Efficient Multi-View Surface Reconstruction and Rendering." pith.science (2026). https://pith.science/paper/I5FVKGR3

@misc{pith2026250817436,
  author       = {Pith},
  title        = {Pith review of: Disentangled Geometry and Appearance for Efficient Multi-View Surface Reconstruction and Rendering},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/I5FVKGR3}},
  note         = {Machine review of arXiv:2508.17436}
}
read the original abstract

This paper addresses the limitations of neural rendering-based multi-view surface reconstruction methods, which require an additional mesh extraction step that is inconvenient and would produce poor-quality surfaces with mesh aliasing, restricting downstream applications. Building on the explicit mesh representation and differentiable rasterization framework, this work proposes an efficient solution that preserves the high efficiency of this framework while significantly improving reconstruction quality and versatility. Specifically, we introduce a disentangled geometry and appearance model that does not rely on deep networks, enhancing learning and broadening applicability. A neural deformation field is constructed to incorporate global geometric context, enhancing geometry learning, while a novel regularization constrains geometric features passed to a neural shader to ensure its accuracy and boost shading. For appearance, a view-invariant diffuse term is separated and baked into mesh vertices, further improving rendering efficiency. Experimental results demonstrate that the proposed method achieves state-of-the-art training (4.84 minutes) and rendering (0.023 seconds) speeds, with reconstruction quality that is competitive with top-performing methods. Moreover, the method enables practical applications such as mesh and texture editing, showcasing its versatility and application potential. This combination of efficiency, competitive quality, and broad applicability makes our approach a valuable contribution to multi-view surface reconstruction and rendering.

Discussion (0). Sign in to comment.

Reference graph

Works this paper leans on

64 extracted references · 61 canonical work pages

  1. [1]

    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 global.max substring 't := if while FUNCTION word.in bbl.in capitalize " " * FUNCT...

    work page

  2. [2]

    L.; Zheng, E.; Frahm, J

    Sch \" o nberger, J. L.; Zheng, E.; Frahm, J. M.; Pollefeys, M. Pixelwise view selection for unstructured multi-view stereo. In: Proceedings of the European Conference on Computer Vision, 501--518, 2016

    work page 2016

  3. [3]

    Accurate, dense, and robust multiview stereopsis Automatic panoramic image stitching using invariant features

    Furukawa, Y.; Ponce, J. Accurate, dense, and robust multiview stereopsis Automatic panoramic image stitching using invariant features. IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 32, No. 8, 1362--1376, 2009

    work page 2009

  4. [4]

    Massively parallel multi-view stereopsis by surface normal diffusion

    Galliani, S.; Lasinger, K.; Schindler, K. Massively parallel multi-view stereopsis by surface normal diffusion. In: Proceedings of the International Conference on Computer Vision, 873--881, 2015

    work page 2015

  5. [5]

    Mvsnet: Depth inference for unstructured multi-view stereo

    Yao, Y.; Luo, Z.; Li, S.; Fang, T.; Quan, L. Mvsnet: Depth inference for unstructured multi-view stereo. In: Proceedings of the European Conference on Computer Vision, 767--783, 2018

    work page 2018

  6. [6]

    Recurrent mvsnet for high-resolution multi-view stereo depth inference

    Yao, Y.; Luo, Z.; Li, S.; Shen, T.; Fang, T.; Quan, L. Recurrent mvsnet for high-resolution multi-view stereo depth inference. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 5525--5534, 2019

    work page 2019

  7. [7]

    Differentiable stereopsis: Meshes from multiple views using differentiable rendering

    Goel, S.; Gkioxari, G.; Malik, J. Differentiable stereopsis: Meshes from multiple views using differentiable rendering. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 8635--8644, 2022

    work page 2022

  8. [8]

    P,; Tancik, M.; Barron, J

    Mildenhall, B.; Srinivasan, P. P,; Tancik, M.; Barron, J. T.; Ramamoorthi, R.; Ng, R. Nerf: Representing scenes as neural radiance fields for view synthesis. In: Proceedings of the European Conference on Computer Vision, 405--421, 2020

    work page 2020

Show all 64 references
  1. [9]

    Unisurf: Unifying neural implicit surfaces and radiance fields for multi-view reconstruction

    Oechsle, M.; Peng, S.; Geiger, A. Unisurf: Unifying neural implicit surfaces and radiance fields for multi-view reconstruction. In: Proceedings of the International Conference on Computer Vision, 5589--5599, 2021

    2021

  2. [10]

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

    Wang, P.; Liu, L.; Liu, Y.; Theobalt, C.; Komura, T.; Wang, W. Neus: Learning neural implicit surfaces by volume rendering for multi-view reconstruction. In: Advances in Neural Information Processing Systems, 27171--27183, 2021

    2021

  3. [11]

    Multiview neural surface reconstruction by disentangling geometry and appearance

    Yariv, L.; Kasten, Y.; Moran, D.; Galun, M.; Atzmon, M.; Ronen, B.; Lipman, Y. Multiview neural surface reconstruction by disentangling geometry and appearance. Advances in Neural Information Processing Systems, 2492--2502, 2020

    2020

  4. [12]

    Volume rendering of neural implicit surfaces

    Yariv, L.; Gu, J.; Kasten, Y.; Lipman, Y. Volume rendering of neural implicit surfaces. In: Advances in Neural Information Processing Systems, 4805--4815, 2021

    2021

  5. [13]

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

    Chen, A.; Xu, Z.; Zhao, F.; Zhang, X.; Xiang, F.; Yu, J.; Su, H. Mvsnerf: Fast generalizable radiance field reconstruction from multi-view stereo. In: Proceedings of the International Conference on Computer Vision, 14124--14133, 2021

    2021

  6. [14]

    Pixelnerf: Neural radiance fields from one or few images

    Yu, A.; Ye, V.; Tancik, M.; Kanazawa, A. Pixelnerf: Neural radiance fields from one or few images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 4578--4587, 2021

    2021

  7. [15]

    Nerfingmvs: Guided optimization of neural radiance fields for indoor multi-view stereo

    Wei, Y.; Liu, S.; Rao, Y.; Zhao, W.; Lu, J.; Zhou, J. Nerfingmvs: Guided optimization of neural radiance fields for indoor multi-view stereo. In: Proceedings of the International Conference on Computer Vision, 5610--5619, 2021

    2021

  8. [16]

    P.; Zhou, H.; Barron, J

    Wang, Q.; Wang, Z.; Genova, K.; Srinivasan, P. P.; Zhou, H.; Barron, J. T.; Martin-Brualla, R.; Snavely, N.; Funkhouser, T. Ibrnet: Learning multi-view image-based rendering. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 4690--4699, 2021

    2021

  9. [17]

    Instant neural graphics primitives with a multiresolution hash encoding

    M \" u ller, T.; Evans, A.; Schied, C.; Keller, A. Instant neural graphics primitives with a multiresolution hash encoding. ACM Transactions on Graphics (TOG) Vol. 41, No. 4, 1--15, 2022

    2022

  10. [18]

    T.; Mildenhall, B.; Tancik, M.; Hedman, P.; Martin-Brualla, R.; Srinivasan, P

    Barron, J. T.; Mildenhall, B.; Tancik, M.; Hedman, P.; Martin-Brualla, R.; Srinivasan, P. P. Mip-nerf: A multiscale representation for anti-aliasing neural radiance fields. In: Proceedings of the International Conference on Computer Vision, 5855--5864, 2021

    2021

  11. [19]

    Neus2: Fast learning of neural implicit surfaces for multi-view reconstruction

    Wang, Y.; Han, Q.; Habermann, M.; Daniilidis, K.; Theobalt, C.; Liu, L. Neus2: Fast learning of neural implicit surfaces for multi-view reconstruction. In: Proceedings of the International Conference on Computer Vision, 3295--3306, 2023

    2023

  12. [20]

    E.; Cline, H

    Lorensen, W. E.; Cline, H. E. Marching cubes: A high resolution 3D surface construction algorithm. In: ACM SIGGRAPH Computer Graphics, 163--169, 1987

    1987

  13. [21]

    Multi-view mesh reconstruction with neural deferred shading

    Worchel, M.; Diaz, R.; Hu, W.; Schreer, O.; Feldmann, I.; Eisert, P. Multi-view mesh reconstruction with neural deferred shading. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 6187--6197, 2022

    2022

  14. [22]

    FastMESH: Fast Surface Reconstruction by Hexagonal Mesh-based Neural Rendering

    Zhang, Y.; Zhu, J.; Lin, L. FastMESH: Fast Surface Reconstruction by Hexagonal Mesh-based Neural Rendering. arXiv preprint arXiv:2305.17858, 2023

    2023 arXiv

  15. [23]

    Sur2f: A Hybrid Representation for High-Quality and Efficient Surface Reconstruction from Multi-view Images

    Huang, Z.; Liang, Z.; Zhang, H.; Lin, Y.; Jia, K. Sur2f: A Hybrid Representation for High-Quality and Efficient Surface Reconstruction from Multi-view Images. arXiv preprint arXiv:2401.03704, 2024

    2024 arXiv

  16. [24]

    CPT-VR: Improving Surface Rendering via Closest Point Transform with View-Reflection Appearance

    Hu, Z.; Zhang, Y.; Liu, C.; Li, L.; Peng, S.; Zhou, X.; Fan, C.; Yu, X. CPT-VR: Improving Surface Rendering via Closest Point Transform with View-Reflection Appearance. In: Proceedings of the European Conference on Computer Vision, 223--239, 2025

    2025

  17. [25]

    P.; Mildenhall, B.; Barron, J

    Hedman, P.; Srinivasan, P. P.; Mildenhall, B.; Barron, J. T.; Debevec, P. Baking neural radiance fields for real-time view synthesis. In: Proceedings of the International Conference on Computer Vision, 5875--5884, 2021

    2021

  18. [26]

    Merf: Memory-efficient radiance fields for real-time view synthesis in unbounded scenes

    Reiser, C.; Szeliski, R.; Verbin, D.; Srinivasan, P.; Mildenhall, B.; Geiger, A.; Barron, J.; Hedman, P. Merf: Memory-efficient radiance fields for real-time view synthesis in unbounded scenes. ACM Transactions on Graphics (TOG) Vol. 42, No. 4, 1--12, 2023

    2023

  19. [27]

    P.; Szeliski, R.; Barron, J

    Yariv, L.; Hedman, P.; Reiser, C.; Verbin, D.; Srinivasan, P. P.; Szeliski, R.; Barron, J. T.; Mildenhall, B. Bakedsdf: Meshing neural sdfs for real-time view synthesis. In: ACM SIGGRAPH Conference Proceedings, 1--9, 2023

    2023

  20. [28]

    Delicate textured mesh recovery from nerf via adaptive surface refinement

    Tang, J.; Zhou, H.; Chen, X.; Hu, T.; Ding, E.; Wang, J.; Zeng, G. Delicate textured mesh recovery from nerf via adaptive surface refinement. In: Proceedings of the International Conference on Computer Vision, 17739--17749, 2023

    2023

  21. [29]

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

    Gu, X.; Fan, Z.; Zhu, S.; Dai, Z.; Tan, F.; Tan, P. Cascade cost volume for high-resolution multi-view stereo and stereo matching. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2495--2504, 2020

    2020

  22. [30]

    Screened poisson surface reconstruction

    Kazhdan, M.; Hoppe, H. Screened poisson surface reconstruction. ACM Transactions on Graphics (ToG) Vol. 32, No. 3, 1--13, 2013

    2013

  23. [31]

    J.; Florence, P.; Straub, J.; Newcombe, R.; Lovegrove, S

    Park, J. J.; Florence, P.; Straub, J.; Newcombe, R.; Lovegrove, S. Deepsdf: Learning continuous signed distance functions for shape representation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 165--174, 2019

    2019

  24. [32]

    Occupancy networks: Learning 3d reconstruction in function space

    Mescheder, L.; Oechsle, M.; Niemeyer, M.; Nowozin, S.; Geiger, A. Occupancy networks: Learning 3d reconstruction in function space. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 4460--4470, 2019

    2019

  25. [33]

    Implicit functions in feature space for 3d shape reconstruction and completion

    Chibane, J.; Alldieck, T.; Pons-Moll, G. Implicit functions in feature space for 3d shape reconstruction and completion. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 6970--6981, 2020

    2020

  26. [34]

    Learning implicit fields for generative shape modeling

    Chen, Z.; Zhang, H. Learning implicit fields for generative shape modeling. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 5939--5948, 2019

    2019

  27. [35]

    Differentiable volumetric rendering: Learning implicit 3d representations without 3d supervision

    Niemeyer, M.; Mescheder, L.; Oechsle, M.; Geiger, A. Differentiable volumetric rendering: Learning implicit 3d representations without 3d supervision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 3504--3515, 2020

    2020

  28. [36]

    S.; Tao, W

    Fu, Q.; Xu, Q.; Ong, Y. S.; Tao, W. Geo-neus: Geometry-consistent neural implicit surfaces learning for multi-view reconstruction. In: Advances in Neural Information Processing Systems, 3403--3416, 2022

    2022

  29. [37]

    C2f2neus: Cascade cost frustum fusion for high fidelity and generalizable neural surface reconstruction

    Xu, L.; Guan, T.; Wang, Y.; Liu, W.; Zeng, Z.; Wang, J.; Yang, W. C2f2neus: Cascade cost frustum fusion for high fidelity and generalizable neural surface reconstruction. In: Proceedings of the IEEE International Conference on Computer Vision, 18291--18301, 2023

    2023

  30. [38]

    J.; Chen, H

    Mu, T. J.; Chen, H. X.; Cai, J. X.; Guo, N. Neural 3D reconstruction from sparse views using geometric priors. Computational Visual Media Vol. 9, No. 4, 687--697, 2023

    2023

  31. [39]

    H.; Unberath, M.; Liu, M

    Li, Z.; M \" u ller, T.; Evans, A.; Taylor, R. H.; Unberath, M.; Liu, M. Y.; Lin, C. H. Neuralangelo: High-fidelity neural surface reconstruction. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 8456--8465, 2023

    2023

  32. [40]

    Point-nerf: Point-based neural radiance fields

    Xu, Q.; Xu, Z.; Philip, J.; Bi, S.; Shu, Z.; Sunkavalli, K.; Neumann, U. Point-nerf: Point-based neural radiance fields. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 5438--5448, 2022

    2022

  33. [41]

    Plenoxels: Radiance fields without neural networks

    Fridovich-Keil, S.; Yu, A.; Tancik, M.; Chen, Q.; Recht, B.; Kanazawa, A. Plenoxels: Radiance fields without neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 5501--5510, 2022

    2022

  34. [42]

    Tensorf: Tensorial radiance fields

    Chen, A.; Xu, Z.; Geiger, A.; Yu, J.; Su, H. Tensorf: Tensorial radiance fields. In: Proceedings of the European Conference on Computer Vision, 333--350, 2022

    2022

  35. [43]

    Sun, C.; Sun, M.; Chen, H. T. Direct voxel grid optimization: Super-fast convergence for radiance fields reconstruction. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 5459--5469, 2022

    2022

  36. [44]

    Voxurf: Voxel-based efficient and accurate neural surface reconstruction

    Wu, T.; Wang, J.; Pan, X.; Xu, X.; Theobalt, C.; Liu, Z.; Lin, D. Voxurf: Voxel-based efficient and accurate neural surface reconstruction. arXiv preprint arXiv:2208.12697, 2022

    2022 arXiv

  37. [45]

    3d gaussian splatting for real-time radiance field rendering

    Kerbl, B.; Kopanas, G.; Leimk \" u hler, T.;Drettakis, G. 3d gaussian splatting for real-time radiance field rendering. ACM Transactions on Graphics (TOG) Vol. 42, No. 4, 139--1, 2023

    2023

  38. [46]

    Differentiable rendering: A survey

    Kato, H.; Beker, D.; Morariu, M.; Ando, T.; Matsuoka, T.; Kehl, W.; Gaidon, A. Differentiable rendering: A survey. arXiv preprint arXiv:2006.12057, 2020

    2006 arXiv

  39. [47]

    M.; Aittala, M.; Durand, F.; Lehtinen, J

    Li, T. M.; Aittala, M.; Durand, F.; Lehtinen, J. Differentiable monte carlo ray tracing through edge sampling. ACM Transactions on Graphics (TOG) Vol. 37, No. 6, 1--11, 2018

    2018

  40. [48]

    Mitsuba 2: A retargetable forward and inverse renderer

    Nimier-David, M.; Vicini, D.; Zeltner, T.; Jakob, W. Mitsuba 2: A retargetable forward and inverse renderer. ACM Transactions on Graphics (ToG) Vol. 38, No. 6, 1--17, 2019

    2019

  41. [49]

    Path-space differentiable rendering

    Zhang, C.; Miller, B.; Yan, K.; Gkioulekas, I.; Zhao, S. Path-space differentiable rendering. ACM Transactions on Graphics (TOG) Vol. 39, No. 4, 143--1, 2020

    2020

  42. [50]

    Reparameterizing discontinuous integrands for differentiable rendering

    Loubet, G.; Holzschuch, N.; Jakob, W. Reparameterizing discontinuous integrands for differentiable rendering. ACM Transactions on Graphics (TOG) Vol. 38, No. 6, 1--14, 2019

    2019

  43. [51]

    Unified shape and svbrdf recovery using differentiable monte carlo rendering

    Luan, F.; Zhao, S.; Bala, K.; Dong, Z. Unified shape and svbrdf recovery using differentiable monte carlo rendering. Computer Graphics Forum Vol. 40, 101--113, 2021

    2021

  44. [52]

    Extracting triangular 3d models, materials, and lighting from images

    Munkberg, J.; Hasselgren, J.; Shen, T.; Gao, J.; Chen, W.; Evans, A.; M \" u ller, T.; Fidler, S. Extracting triangular 3d models, materials, and lighting from images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 8280--8290, 2022

    2022

  45. [53]

    Shape, light, and material decomposition from images using monte carlo rendering and denoising

    Hasselgren, J.; Hofmann, N.; Munkberg, J. Shape, light, and material decomposition from images using monte carlo rendering and denoising. In: Advances in Neural Information Processing Systems, 22856--22869, 2022

    2022

  46. [54]

    Y.; Fidler, S

    Shen, T.; Gao, J.; Yin, K.; Liu, M. Y.; Fidler, S. Deep marching tetrahedra: a hybrid representation for high-resolution 3d shape synthesis. In: Advances in Neural Information Processing Systems, 6087--6101, 2021

    2021

  47. [55]

    Modular primitives for high-performance differentiable rendering

    Laine, S.; Hellsten, J.; Karras, T.; Seol, Y.; Lehtinen, J.; Aila, T. Modular primitives for high-performance differentiable rendering. ACM Transactions on Graphics (ToG) Vol. 39, No. 6, 1--14, 2020

    2020

  48. [56]

    Differential coordinates for local mesh morphing and deformation

    Alexa, M. Differential coordinates for local mesh morphing and deformation. The Visual Computer Vol. 19, No. 2, 105--114, 2003

    2003

  49. [57]

    Z.; DeVito, Z.; Raison, M.; Tejani, A.; Chilamkurthy, S.; Steiner, B.; Fang, L.; Bai, J.; Chintala, S

    Paszke, A.; Gross, S.; Massa, F.; Lerer, A.; Bradbury, J.; Chanan, G.; Killeen, T.; Lin, Z.; Gimelshein, N.; Antiga, L.; Desmaison, A.; K \" o pf, A.; Yang, E. Z.; DeVito, Z.; Raison, M.; Tejani, A.; Chilamkurthy, S.; Steiner, B.; Fang, L.; Bai, J.; Chintala, S. PyTorch: An Im...

    2019

  50. [58]

    Kinga, D.; Adam, J. B. Adam: A Method for Stochastic Optimization. In: International Conference on Learning Representations (ICLR), 6, 2015

    2015

  51. [59]

    Explicit neural surfaces: Learning continuous geometry with deformation fields

    Walker, T.; Mariotti, O.; Vaxman, A.; Bilen, H. Explicit neural surfaces: Learning continuous geometry with deformation fields. arXiv preprint arXiv:2306.02956, 2023

    2023 arXiv

  52. [60]

    Smooth subdivision surfaces based on triangles

    Loop, C. Smooth subdivision surfaces based on triangles. Master’s thesis, University of Utah, Department of Mathematics, 1987

    1987

  53. [61]

    Large scale multi-view stereopsis evaluation

    Jensen, R.; Dahl, A.; Vogiatzis, G.; Tola, E.; Aan s H. Large scale multi-view stereopsis evaluation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 406--413, 2014

    2014

  54. [62]

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

    Yao, Y.; Luo, Z.; Li, S.; Zhang, J.; Ren, Y.; Zhou, L.; Fang, T.; Quan, L. Blendedmvs: A large-scale dataset for generalized multi-view stereo networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1790--1799, 2020

    2020

  55. [63]

    T.; Sun, J

    Gao, L.; Yang, J.; Zhang, B. T.; Sun, J. M.; Yuan, Y. J.; Fu, H.; Lai, Y. K. Mesh-based gaussian splatting for real-time large-scale deformation. arXiv preprint arXiv:2402.04796, 2024

    2024 arXiv

  56. [64]

    Sugar: Surface-aligned gaussian splatting for efficient 3d mesh reconstruction and high-quality mesh rendering

    Gu \' e don, A.; Lepetit, V. Sugar: Surface-aligned gaussian splatting for efficient 3d mesh reconstruction and high-quality mesh rendering. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 5354--5363, 2024

    2024

Pith tools

Reviewed August 5, 2026 · model on record in the stance chip above.