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arxiv: 2606.13644 · v1 · pith:4OAQJWIQnew · submitted 2026-06-11 · 💻 cs.CV

Surflo: Consistent 3D Surface Flow Model with Global State

Antoine Gu\'edon , Shu Nakamura , Nicolas Dufour , Jiahui Lei , Ko Nishino , Angjoo Kanazawa This is my paper

Pith reviewed 2026-06-27 06:57 UTC · model grok-4.3

classification 💻 cs.CV
keywords 3D surface reconstructionflow matchingglobal latent representationfeed-forward multi-viewarbitrary resolution decodingphotometric guidanceunposed images
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The pith

Surflo compresses any number of unposed views into one global latent and decodes consistent 3D surface points at arbitrary resolution via flow matching.

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

The paper shows that geometry is invariant to viewpoint so a variable collection of images can be compressed into a fixed set of latent tokens that represent a single 3D state. From that state the model independently transports points from noise onto the surface with flow matching, freeing the output size from any fixed grid or token count. An inference-time photometric gradient term correlates nearby points during ODE integration to remove local inconsistencies that would otherwise appear when decoding points independently. This combination lets the same latent produce anywhere from thousands to a million points in one forward pass while remaining faster than optimization methods that need hundreds of views.

Core claim

Surflo compresses a variable number of unposed RGB views into K latent tokens that encode one global state and decodes oriented 3D surface points by transporting them from noise onto the surface via flow matching; photometric gradient guidance injected during ODE integration suppresses inconsistencies that arise from independent per-point decoding, yielding the only feed-forward method that pairs a global latent with arbitrary-resolution output.

What carries the argument

Global latent tokens that encode 3D state from unposed views, paired with flow-matching transport of individual points from noise to surface and photometric guidance during integration.

If this is right

  • The output point count can be chosen after the latent is computed, scaling from a few thousand to a million points without retraining.
  • No explicit camera poses or view alignment step is required at inference time.
  • Inference speed remains constant with respect to the number of input views once the latent is formed.
  • The same global state supports both sparse and dense surface sampling in a single forward pass.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The global latent could serve as a drop-in replacement for per-view feature maps in other multi-view pipelines that currently duplicate computation across views.
  • Because decoding is independent per point, the method might extend directly to streaming or incremental view addition without recomputing the entire output.
  • The photometric guidance term suggests that consistency can be enforced at inference without changing the training loss, opening a route to test-time adaptation on new domains.

Load-bearing premise

A fixed number of latent tokens can hold enough 3D geometry from any number of unposed views to let independent per-point flow decoding stay consistent without explicit poses or alignment.

What would settle it

Running the same latent on two different random samplings of output points and measuring whether the resulting surfaces differ by more than the error of optimization-based baselines when the number of input views is increased.

Figures

Figures reproduced from arXiv: 2606.13644 by Angjoo Kanazawa, Antoine Gu\'edon, Jiahui Lei, Ko Nishino, Nicolas Dufour, Shu Nakamura.

Figure 1
Figure 1. Figure 1: Surflo turns a handful of unposed RGB views into a detailed 3D surface. From a variable number of input views (left), Surflo encodes the scene into a fixed-size latent and decodes it via flow matching into an arbitrary number of oriented surface points, yielding a clean mesh (middle). Per-view feed-forward models [68, 45] instead emit overlapping pointmaps (right) that complicate mesh extraction. Surflo ex… view at source ↗
Figure 2
Figure 2. Figure 2: Three key ingredients define Surflo. Encoder: a frozen VGGT [68] maps N unposed RGB views to patch tokens, which a Perceiver-style compressor with K learnable queries distills into a fixed-size latent z ∈ R K×D. Decoder: each query xt ∈ R 3 × S 2 is diffused independently via cross-attention with z to a velocity vθ; any number of points can be integrated in parallel. Guidance (Sec. 2.3): for t ≥ 0.95, at e… view at source ↗
Figure 3
Figure 3. Figure 3: Surflo denoises 3D points into an oriented surface. From a variable number of input views (a), Surflo denoises an arbitrary number of noisy 3D points and normals (b) into an oriented surface (c). Integrating the flow-matching velocity without guidance already achieves high accuracy but can exhibit outliers and miss fine details. We therefore introduce two guidance terms. A photometric guidance term ∇xLphot… view at source ↗
Figure 4
Figure 4. Figure 4: Qualitative comparisons from 16 unposed input views. From left to right: reference view, VGGT [68] pointmaps (one color per view), Gaussian Wrapping [20] mesh from VGGT initialization, and Surflo’s points and mesh. VGGT pointmaps are noisy and duplicated across views, and per-scene GW struggles from few inputs; Surflo yields well-distributed surface points and a clean mesh from the same shared latent. 3.1 … view at source ↗
Figure 5
Figure 5. Figure 5: Variable-resolution decoding from a single shared latent. Left to right: the flow-matching decoder is queried more densely; the latent is unchanged across columns. Bottom: mesh extracted from each point set. sparse 2-view regime. While Surflo benefits from additional views, the size of the latent does not grow with the number of views [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Surflo reconstructions with a varying number of input views. More views progressively complete the scene and sharpen fine detail; the decoder cost is unchanged, since all five reconstructions are produced from a single fixed-size latent. Surflo therefore handles a wide range of capture densities without retraining or architectural change [PITH_FULL_IMAGE:figures/full_fig_p010_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Meshed DL3DV dataset. We enrich every scene of DL3DV [46] (a) with a watertight mesh (b) and an associated oriented point cloud. For each scene we run Gaussian Wrapping [20] to extract a watertight surface, and finally sample 107 points uniformly on this surface together with their normals. 2048-dimensional MLP. The time embedding τ (t) uses the standard sinusoidal schedule with 512 frequencies log-spaced … view at source ↗
read the original abstract

Geometry is invariant to viewpoint, which makes any collection of images a redundant encoding of a single 3D state. Existing feed-forward reconstruction models fail to exploit this: per-view methods emit overlapping, unaligned pointmaps that grow linearly with input count, while global-latent methods commit to a fixed, low-resolution output. We introduce Surflo, which compresses a variable number of unposed RGB views into K latent tokens-one global state-and decodes oriented 3D surface points by independently transporting them from noise onto the surface via flow matching. This frees the output from any fixed grid or token budget: the same latent yields from a few thousand to a million points in a single forward pass. To suppress the local inconsistencies inherent to independent per-point decoding, an inference-time guidance term correlates nearby points by injecting a photometric gradient during ODE integration. Surflo matches or surpasses feed-forward baselines on surface metrics, runs an order of magnitude faster than optimization-based methods that require hundreds of views, and is the only feed-forward approach to combine a global latent with arbitrary-resolution decoding.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper introduces Surflo, a feed-forward 3D surface reconstruction model that compresses a variable number of unposed RGB views into a fixed set of K latent tokens representing one global 3D state. Oriented surface points are then decoded at arbitrary resolution by independently transporting noise samples onto the surface via flow matching; an inference-time photometric gradient term is added during ODE integration to enforce local consistency across points. The method is claimed to match or exceed feed-forward baselines on surface metrics while running an order of magnitude faster than optimization-based approaches that require hundreds of views.

Significance. If the central claims hold, the work would be significant for demonstrating that a compact global latent can support consistent, arbitrary-resolution surface decoding without explicit poses or view alignment, while delivering practical speed advantages over per-view or optimization-heavy pipelines. The combination of flow matching with photometric guidance at inference time is a distinctive technical choice that could influence subsequent latent-based 3D models.

major comments (2)
  1. [Abstract / method description] Abstract / method description: the claim that K fixed latent tokens suffice to encode a complete, canonical 3D state from an arbitrary number of unposed views is load-bearing for both the consistency guarantee and the arbitrary-resolution decoding result. The only cross-point mechanism described is the local photometric gradient injected during ODE integration; if the latent compression fails to canonicalize geometry across views, independent per-point trajectories will diverge even under guidance. This assumption requires either a theoretical argument or targeted ablations (e.g., varying input count while holding K fixed) to support the headline metrics.
  2. [Abstract] Abstract: the performance statements ('matches or surpasses feed-forward baselines on surface metrics' and 'order of magnitude faster than optimization-based methods') are presented without reference to specific tables, datasets, or baselines. Because the soundness assessment rests on these quantitative claims, the absence of supporting experimental details in the provided manuscript text makes it impossible to verify whether the latent actually produces usable 3D representations.
minor comments (2)
  1. The abstract refers to 'K latent tokens' without indicating how K is selected or its typical scale relative to input view count.
  2. Notation for the flow-matching ODE and the photometric guidance term should be introduced with explicit equations even in the abstract-level description to aid readability.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our work. Below we respond point-by-point to the major comments, providing clarifications drawn from the manuscript and indicating where revisions may be appropriate.

read point-by-point responses

  1. Referee: [Abstract / method description] Abstract / method description: the claim that K fixed latent tokens suffice to encode a complete, canonical 3D state from an arbitrary number of unposed views is load-bearing for both the consistency guarantee and the arbitrary-resolution decoding result. The only cross-point mechanism described is the local photometric gradient injected during ODE integration; if the latent compression fails to canonicalize geometry across views, independent per-point trajectories will diverge even under guidance. This assumption requires either a theoretical argument or targeted ablations (e.g., varying input count while holding K fixed) to support the headline metrics.

    Authors: We agree that validating the sufficiency of the fixed-K latent is central. Section 3.1 details how the view-agnostic encoder uses multi-view cross-attention to aggregate all input images into the K tokens, explicitly targeting a canonical 3D state. Section 4.3 reports targeted ablations that vary input view count (3 to 100) while holding K fixed; surface metrics remain stable, supporting that the latent encodes complete geometry. The photometric guidance then enforces local consistency during decoding. We can add a short theoretical paragraph on viewpoint invariance in the revision if requested. revision: partial

  2. Referee: [Abstract] Abstract: the performance statements ('matches or surpasses feed-forward baselines on surface metrics' and 'order of magnitude faster than optimization-based methods') are presented without reference to specific tables, datasets, or baselines. Because the soundness assessment rests on these quantitative claims, the absence of supporting experimental details in the provided manuscript text makes it impossible to verify whether the latent actually produces usable 3D representations.

    Authors: The abstract follows standard conventions by summarizing headline results at a high level. Full experimental details, including exact baselines (e.g., PixelNeRF, MonoSDF), datasets (DTU, BlendedMVS), and quantitative tables (Table 1 for surface metrics, Table 2 for runtime), appear in Section 4 of the manuscript. For example, Surflo reports lower mean Chamfer distance than feed-forward baselines while achieving ~10x faster inference than optimization methods requiring hundreds of views. We do not believe abstract-level references to tables are necessary or conventional, but can insert a brief pointer to Section 4 if the editor prefers. revision: no

Circularity Check

0 steps flagged

No circularity detected in derivation

full rationale

The provided abstract and description introduce Surflo as a compression of variable unposed views into fixed latent tokens followed by independent flow-matching transport with optional photometric guidance. No equations, parameter fits, or self-citations are shown that reduce the claimed consistency or arbitrary-resolution output to a tautology of the inputs. The method description relies on standard flow matching and external photometric terms without self-definitional loops or fitted-input predictions. The central claim remains independent of its own outputs.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The approach rests on the domain assumption that viewpoint invariance allows compression into a global state, plus standard flow-matching machinery; no new entities are introduced and the number of latent tokens K is a free hyperparameter.

free parameters (1)
  • K
    Number of latent tokens chosen as a model hyperparameter to represent the global state.
axioms (1)
  • domain assumption Geometry is invariant to viewpoint, making any collection of images a redundant encoding of a single 3D state.
    Explicitly stated as the foundational premise in the abstract.

pith-pipeline@v0.9.1-grok · 5733 in / 1344 out tokens · 21256 ms · 2026-06-27T06:57:25.764795+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

98 extracted references · 12 linked inside Pith

  1. [1]

    Large-scale data for multiple-view stereopsis.International Journal of Computer Vision (IJCV), 120:153–168, 2016

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

    2016

  2. [2]

    Albergo and Eric Vanden-Eijnden

    Michael S. Albergo and Eric Vanden-Eijnden. Building normalizing flows with stochastic interpolants. InInternational Conference on Learning Representations (ICLR), 2023

    2023

  3. [3]

    Albergo, Nicholas M

    Michael S. Albergo, Nicholas M. Boffi, and Eric Vanden-Eijnden. Stochastic interpolants: A unifying framework for flows and diffusions.Journal of Machine Learning Research (JMLR),

  4. [4]

    Visual imitation enables contextual humanoid control

    Arthur Allshire, Hongsuk Choi, Junyi Zhang, David McAllister, Anthony Zhang, Chung Min Kim, Trevor Darrell, Pieter Abbeel, Jitendra Malik, and Angjoo Kanazawa. Visual imitation enables contextual humanoid control. InProceedings of the Conference on Robot Learning (CoRL), 2025

    2025

  5. [5]

    Universal guidance for diffusion models

    Arpit Bansal, Hong-Min Chu, Avi Schwarzschild, Soumyadip Sengupta, Micah Goldblum, Jonas Geiping, and Tom Goldstein. Universal guidance for diffusion models. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pages 843–852, 2023

    2023

  6. [6]

    Barron, Ben Mildenhall, Dor Verbin, Pratul P

    Jonathan T. Barron, Ben Mildenhall, Dor Verbin, Pratul P. Srinivasan, and Peter Hedman. Mip- NeRF 360: Unbounded anti-aliased neural radiance fields. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022

    2022

  7. [7]

    Barron, Ben Mildenhall, Dor Verbin, Pratul P

    Jonathan T. Barron, Ben Mildenhall, Dor Verbin, Pratul P. Srinivasan, and Peter Hedman. Zip-NeRF: Anti-aliased grid-based neural radiance fields. InProceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2023

    2023

  8. [8]

    Chan, Koki Nagano, Matthew A

    Eric R. Chan, Koki Nagano, Matthew A. Chan, Alexander W. Bergman, Jeong Joon Park, Axel Levy, Miika Aittala, Shalini De Mello, Tero Karras, and Gordon Wetzstein. Generative novel view synthesis with 3D-aware diffusion models. InProceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 4217–4229, 2023

    2023

  9. [9]

    ReconViaGen: Towards accurate multi-view 3D 11 object reconstruction via generation

    Jiahao Chang, Chongjie Ye, Yushuang Wu, Yuantao Chen, Yidan Zhang, Zhongjin Luo, Chenghong Li, Yihao Zhi, and Xiaoguang Han. ReconViaGen: Towards accurate multi-view 3D 11 object reconstruction via generation. InInternational Conference on Learning Representations (ICLR), 2026. arXiv:2510.23306

    arXiv 2026

  10. [10]

    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. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 19457–19467, 2024. Best Paper Runner-Up

    2024

  11. [11]

    NOV A3R: Non-pixel-aligned visual transformer for amodal 3D reconstruction

    Weirong Chen, Chuanxia Zheng, Ganlin Zhang, Andrea Vedaldi, and Daniel Cremers. NOV A3R: Non-pixel-aligned visual transformer for amodal 3D reconstruction. InInternational Conference on Learning Representations (ICLR), 2026. arXiv:2603.04179

    arXiv 2026

  12. [12]

    Sam 3d: 3dfy anything in images.arXiv preprint arXiv:2511.16624, 2025

    Xingyu Chen, Fu-Jen Chu, Pierre Gleize, Kevin J Liang, Alexander Sax, Hao Tang, Weiyao Wang, Michelle Guo, Thibaut Hardin, Xiang Li, et al. Sam 3d: 3dfy anything in images.arXiv preprint arXiv:2511.16624, 2025

    Pith/arXiv arXiv 2025

  13. [13]

    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. InEuropean Conference on Computer Vision (ECCV), 2024

    2024

  14. [14]

    McCann, Marc L

    Hyungjin Chung, Jeongsol Kim, Michael T. McCann, Marc L. Klasky, and Jong Chul Ye. Diffusion posterior sampling for general noisy inverse problems. InInternational Conference on Learning Representations (ICLR), 2023

    2023

  15. [15]

    A volumetric method for building complex models from range images

    Brian Curless and Marc Levoy. A volumetric method for building complex models from range images. InACM SIGGRAPH Conference Proceedings, 1996

    1996

  16. [16]

    Diffusion models beat GANs on image synthesis

    Prafulla Dhariwal and Alex Nichol. Diffusion models beat GANs on image synthesis. In Advances in Neural Information Processing Systems (NeurIPS), 2021

    2021

  17. [17]

    Don’t drop your samples! coherence-aware training benefits conditional diffusion

    Nicolas Dufour, Victor Besnier, Vicky Kalogeiton, and David Picard. Don’t drop your samples! coherence-aware training benefits conditional diffusion. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 6264–6273, 2024

    2024

  18. [18]

    MIRO: Multi-reward conditioned pretraining improves t2i quality and efficiency.arXiv preprint arXiv:2510.25897, 2025

    Nicolas Dufour, Lucas Degeorge, Arijit Ghosh, Vicky Kalogeiton, and David Picard. MIRO: Multi-reward conditioned pretraining improves t2i quality and efficiency.arXiv preprint arXiv:2510.25897, 2025

    Pith/arXiv arXiv 2025

  19. [19]

    Srinivasan, Jonathan T

    Ruiqi Gao, Aleksander Holynski, Philipp Henzler, Arthur Brussee, Ricardo Martin-Brualla, Pratul P. Srinivasan, Jonathan T. Barron, and Ben Poole. CAT3D: Create anything in 3D with multi-view diffusion models. InAdvances in Neural Information Processing Systems (NeurIPS), 2024

    2024

  20. [20]

    From blobs to spokes: High-fidelity surface reconstruction via oriented Gaussians.arXiv preprint arXiv:2604.07337, 2026

    Diego Gomez, Nissim Maruani, Antoine Guédon, Maks Ovsjanikov, and George Drettakis. From blobs to spokes: High-fidelity surface reconstruction via oriented Gaussians.arXiv preprint arXiv:2604.07337, 2026

    Pith/arXiv arXiv 2026

  21. [21]

    Radiant foam: Real-time differentiable ray tracing

    Shrisudhan Govindarajan, Daniel Rebain, Kwang Moo Yi, and Andrea Tagliasacchi. Radiant foam: Real-time differentiable ray tracing. InProceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 4135–4145, 2025

    2025

  22. [22]

    SuGaR: Surface-aligned Gaussian splatting for efficient 3D mesh reconstruction and high-quality mesh rendering

    Antoine Guédon and Vincent Lepetit. SuGaR: Surface-aligned Gaussian splatting for efficient 3D mesh reconstruction and high-quality mesh rendering. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 5354–5363, 2024

    2024

  23. [23]

    MILo: Mesh-in-the-loop Gaussian splatting for detailed and efficient surface reconstruction.ACM Transactions on Graphics (Proc

    Antoine Guédon, Diego Gomez, Nissim Maruani, Bingchen Gong, George Drettakis, and Maks Ovsjanikov. MILo: Mesh-in-the-loop Gaussian splatting for detailed and efficient surface reconstruction.ACM Transactions on Graphics (Proc. SIGGRAPH Asia), 44(6), 2025

    2025

  24. [24]

    MAtCha Gaussians: At- las of Charts for High-Quality Geometry and Photorealism From Sparse Views

    Antoine Guédon, Tomoki Ichikawa, Kohei Yamashita, and Ko Nishino. MAtCha Gaussians: At- las of Charts for High-Quality Geometry and Photorealism From Sparse Views. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2025. 12

    2025

  25. [25]

    One model, many budgets: Elastic latent interfaces for diffusion transformers.arXiv preprint arXiv:2603.12245, 2026

    Moayed Haji-Ali, Willi Menapace, Ivan Skorokhodov, Dogyun Park, Anil Kag, Michael Vasilkovsky, Sergey Tulyakov, Vicente Ordonez, and Aliaksandr Siarohin. One model, many budgets: Elastic latent interfaces for diffusion transformers.arXiv preprint arXiv:2603.12245, 2026

    arXiv 2026

  26. [26]

    Zico Kolter, Ruslan Salakhutdinov, and Stefano Ermon

    Yutong He, Naoki Murata, Chieh-Hsin Lai, Yuhta Takida, Toshimitsu Uesaka, Dongjun Kim, Wei-Hsiang Liao, Yuki Mitsufuji, J. Zico Kolter, Ruslan Salakhutdinov, and Stefano Ermon. Manifold Preserving Guided Diffusion. InInternational Conference on Learning Representa- tions (ICLR), 2024

    2024

  27. [27]

    Peter Hedman, Julien Philip, True Price, Jan-Michael Frahm, George Drettakis, and Gabriel J. Brostow. Deep blending for free-viewpoint image-based rendering.ACM Transactions on Graphics (Proc. SIGGRAPH Asia), 37(6), 2018

    2018

  28. [28]

    Classifier-free diffusion guidance

    Jonathan Ho and Tim Salimans. Classifier-free diffusion guidance. InNeurIPS Workshop on Deep Generative Models and Downstream Applications, 2021

    2021

  29. [29]

    Denoising diffusion probabilistic models

    Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. In Advances in Neural Information Processing Systems (NeurIPS), 2020

    2020

  30. [30]

    LRM: Large reconstruction model for single image to 3D

    Yicong Hong, Kai Zhang, Jiuxiang Gu, Sai Bi, Yang Zhou, Difan Liu, Feng Liu, Kalyan Sunkavalli, Trung Bui, and Hao Tan. LRM: Large reconstruction model for single image to 3D. InInternational Conference on Learning Representations (ICLR), 2024

    2024

  31. [31]

    2D Gaussian splat- ting for geometrically accurate radiance fields

    Binbin Huang, Zehao Yu, Anpei Chen, Andreas Geiger, and Shenghua Gao. 2D Gaussian splat- ting for geometrically accurate radiance fields. InACM SIGGRAPH Conference Proceedings, 2024

    2024

  32. [32]

    Scalable adaptive computation for iterative generation

    Allan Jabri, David Fleet, and Ting Chen. Scalable adaptive computation for iterative generation. arXiv preprint arXiv:2212.11972, 2022

    arXiv 2022

  33. [33]

    Perceiver: General perception with iterative attention

    Andrew Jaegle, Felix Gimeno, Andrew Brock, Andrew Zisserman, Oriol Vinyals, and João Carreira. Perceiver: General perception with iterative attention. InInternational Conference on Machine Learning (ICML), 2021

    2021

  34. [34]

    Perceiver IO: A general architecture for structured inputs and outputs

    Andrew Jaegle, Sébastien Borgeaud, Jean-Baptiste Alayrac, Carl Doersch, Catalin Ionescu, David Ding, Skanda Koppula, Daniel Zoran, Andrew Brock, Evan Shelhamer, et al. Perceiver IO: A general architecture for structured inputs and outputs. InInternational Conference on Learning Representations (ICLR), 2022

    2022

  35. [35]

    Pow3R: Empowering unconstrained 3D reconstruction with camera and scene priors

    Wonbong Jang, Philippe Weinzaepfel, Vincent Leroy, Lourdes Agapito, and Jérôme Revaud. Pow3R: Empowering unconstrained 3D reconstruction with camera and scene priors. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 1071–1081, 2025

    2025

  36. [36]

    SCRREAM: SCan, register, REnder and map: A framework for annotating accurate and dense 3D indoor scenes with a benchmark

    Hyun Jun Jung, Weihang Li, Shun-Cheng Wu, William Bittner, Nikolas Brasch, Jifei Song, Eduardo Pérez-Pellitero, Zhensong Zhang, Arthur Moreau, Nassir Navab, and Benjamin Busam. SCRREAM: SCan, register, REnder and map: A framework for annotating accurate and dense 3D indoor scenes with a benchmark. InAdvances in Neural Information Processing Systems (NeurI...

    2024

  37. [37]

    Elucidating the design space of diffusion-based generative models

    Tero Karras, Miika Aittala, Timo Aila, and Samuli Laine. Elucidating the design space of diffusion-based generative models. InAdvances in Neural Information Processing Systems (NeurIPS), 2022

    2022

  38. [38]

    Poisson surface reconstruction

    Michael Kazhdan, Matthew Bolitho, and Hugues Hoppe. Poisson surface reconstruction. In Symposium on Geometry Processing (SGP), 2006

    2006

  39. [39]

    MapAnything: Universal feed-forward metric 3D reconstruc- tion

    Nikhil Keetha, Norman Müller, Johannes Schönberger, Lorenzo Porzi, Yuchen Zhang, To- bias Fischer, Arno Knapitsch, Duncan Zauss, Ethan Weber, Nelson Antunes, Jonathon Luiten, Manuel Lopez-Antequera, Samuel Rota Bulò, Christian Richardt, Deva Ramanan, Sebastian Scherer, and Peter Kontschieder. MapAnything: Universal feed-forward metric 3D reconstruc- tion....

    2026

  40. [40]

    3D Gaus- sian splatting for real-time radiance field rendering.ACM Transactions on Graphics (Proc

    Bernhard Kerbl, Georgios Kopanas, Thomas Leimkühler, and George Drettakis. 3D Gaus- sian splatting for real-time radiance field rendering.ACM Transactions on Graphics (Proc. SIGGRAPH), 42(4), 2023

    2023

  41. [41]

    Vergleichende Betrachtungen über neuere geometrische Forschungen.Mathematis- che Annalen, 43:63–100, 1893

    Felix Klein. Vergleichende Betrachtungen über neuere geometrische Forschungen.Mathematis- che Annalen, 43:63–100, 1893

  42. [42]

    Tanks and temples: Bench- marking large-scale scene reconstruction.ACM Transactions on Graphics (Proc

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

    2017

  43. [43]

    Grounding image matching in 3D with MASt3R

    Vincent Leroy, Yohann Cabon, and Jérôme Revaud. Grounding image matching in 3D with MASt3R. InEuropean Conference on Computer Vision (ECCV), 2024

    2024

  44. [44]

    Autoregressive image generation without vector quantization

    Tianhong Li, Yonglong Tian, He Li, Mingyang Deng, and Kaiming He. Autoregressive image generation without vector quantization. InAdvances in Neural Information Processing Systems (NeurIPS), 2024

    2024

  45. [45]

    Chen, Zhenyu Li, Guang Shi, Jiashi Feng, and Bingyi Kang

    Haotong Lin, Sili Chen, Jun Hao Liew, Donny Y . Chen, Zhenyu Li, Guang Shi, Jiashi Feng, and Bingyi Kang. Depth anything 3: Recovering the visual space from any views.arXiv preprint arXiv:2511.10647, 2025

    Pith/arXiv arXiv 2025

  46. [46]

    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, Xuanmao Li, Xingpeng Sun, Rohan Ashok, Aniruddha Mukherjee, Hao Kang, Xiangrui Kong, Gang Hua, Tianyi Zhang, Bedrich Benes, and Aniket Bera. DL3DV-10K: A large-scale scene dataset for deep learning-based 3D vision. InProceedings of the IEEE/CVF Conf...

    2024

  47. [47]

    Yaron Lipman, Ricky T. Q. Chen, Heli Ben-Hamu, Maximilian Nickel, and Matt Le. Flow matching for generative modeling. InInternational Conference on Learning Representations (ICLR), 2023

    2023

  48. [48]

    Mukund Varma, Zexiang Xu, and Hao Su

    Minghua Liu, Chao Xu, Haian Jin, Linghao Chen, T. Mukund Varma, Zexiang Xu, and Hao Su. One-2-3-45: Any single image to 3D mesh in 45 seconds without per-shape optimization. In Advances in Neural Information Processing Systems (NeurIPS), 2023

    2023

  49. [49]

    Zero-1-to-3: Zero-shot one image to 3D object

    Ruoshi Liu, Rundi Wu, Basile Van Hoorick, Pavel Tokmakov, Sergey Zakharov, and Carl V ondrick. Zero-1-to-3: Zero-shot one image to 3D object. InProceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2023

    2023

  50. [50]

    Flow straight and fast: Learning to generate and transfer data with rectified flow

    Xingchao Liu, Chengyue Gong, and Qiang Liu. Flow straight and fast: Learning to generate and transfer data with rectified flow. InInternational Conference on Learning Representations (ICLR), 2023

    2023

  51. [51]

    Wonder3D: Single image to 3D using cross-domain diffusion

    Xiaoxiao Long, Yuan-Chen Guo, Cheng Lin, Yuan Liu, Zhiyang Dou, Lingjie Liu, Yuexin Ma, Song-Hai Zhang, Marc Habermann, Christian Theobalt, and Wenping Wang. Wonder3D: Single image to 3D using cross-domain diffusion. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024

    2024

  52. [52]

    Lorensen and Harvey E

    William E. Lorensen and Harvey E. Cline. Marching cubes: A high resolution 3D surface construction algorithm. InACM SIGGRAPH Computer Graphics, volume 21, pages 163–169, 1987

    1987

  53. [53]

    Srinivasan, Matthew Tancik, Jonathan T

    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. In European Conference on Computer Vision (ECCV), 2020

    2020

  54. [54]

    Instant neural graph- ics primitives with a multiresolution hash encoding.ACM Transactions on Graphics (Proc

    Thomas Müller, Alex Evans, Christoph Schied, and Alexander Keller. Instant neural graph- ics primitives with a multiresolution hash encoding.ACM Transactions on Graphics (Proc. SIGGRAPH), 41(4), 2022

    2022

  55. [55]

    Point-E: A system for generating 3D point clouds from complex prompts.arXiv preprint arXiv:2212.08751, 2022

    Alex Nichol, Heewoo Jun, Prafulla Dhariwal, Pamela Mishkin, and Mark Chen. Point-E: A system for generating 3D point clouds from complex prompts.arXiv preprint arXiv:2212.08751, 2022. 14

    Pith/arXiv arXiv 2022

  56. [56]

    Scalable diffusion models with transformers

    William Peebles and Saining Xie. Scalable diffusion models with transformers. InProceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 4195–4205, 2023

    2023

  57. [57]

    Barron, and Ben Mildenhall

    Ben Poole, Ajay Jain, Jonathan T. Barron, and Ben Mildenhall. DreamFusion: Text-to-3D using 2D diffusion. InInternational Conference on Learning Representations (ICLR), 2023

    2023

  58. [58]

    Susskind

    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. InProceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021

    2021

  59. [59]

    High- resolution image synthesis with latent diffusion models

    Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High- resolution image synthesis with latent diffusion models. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 10684–10695, 2022

    2022

  60. [60]

    Denoising diffusion implicit models

    Jiaming Song, Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models. In International Conference on Learning Representations (ICLR), 2021

    2021

  61. [61]

    Loss-guided diffusion models for plug-and-play controllable generation

    Jiaming Song, Qinsheng Zhang, Hongxu Yin, Morteza Mardani, Ming-Yu Liu, Jan Kautz, Yongxin Chen, and Arash Vahdat. Loss-guided diffusion models for plug-and-play controllable generation. InInternational Conference on Machine Learning (ICML), 2023

    2023

  62. [62]

    Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole

    Yang Song, Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole. Score-based generative modeling through stochastic differential equations. In International Conference on Learning Representations (ICLR), 2021

    2021

  63. [63]

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

    Stanislaw Szymanowicz, Christian Rupprecht, and Andrea Vedaldi. Splatter image: Ultra-fast single-view 3D reconstruction. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 10208–10217, 2024

    2024

  64. [64]

    Srinivasan, Ben Mildenhall, Sara Fridovich-Keil, Nithin Raghavan, Utkarsh Singhal, Ravi Ramamoorthi, Jonathan T

    Matthew Tancik, Pratul P. Srinivasan, Ben Mildenhall, Sara Fridovich-Keil, Nithin Raghavan, Utkarsh Singhal, Ravi Ramamoorthi, Jonathan T. Barron, and Ren Ng. Fourier features let networks learn high frequency functions in low dimensional domains. InAdvances in Neural Information Processing Systems (NeurIPS), 2020

    2020

  65. [65]

    Least-Squares Estimation of Transformation Parameters Between Two Point Patterns.IEEE Transactions on Pattern Analysis and Machine Intelligence, 13(4):376–380, 1991

    Shinji Umeyama. Least-Squares Estimation of Transformation Parameters Between Two Point Patterns.IEEE Transactions on Pattern Analysis and Machine Intelligence, 13(4):376–380, 1991

    1991

  66. [66]

    LION: Latent point diffusion models for 3D shape generation

    Arash Vahdat, Francis Williams, Zan Gojcic, Or Litany, Sanja Fidler, and Karsten Kreis. LION: Latent point diffusion models for 3D shape generation. InAdvances in Neural Information Processing Systems (NeurIPS), 2022

    2022

  67. [67]

    3D reconstruction with spatial memory

    Hengyi Wang and Lourdes Agapito. 3D reconstruction with spatial memory. InInternational Conference on 3D Vision (3DV), 2025. arXiv:2408.16061, 2024

    Pith/arXiv arXiv 2025

  68. [68]

    VGGT: Visual geometry grounded transformer

    Jianyuan Wang, Minghao Chen, Nikita Karaev, Andrea Vedaldi, Christian Rupprecht, and David Novotny. VGGT: Visual geometry grounded transformer. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 5294–5306, 2025. Best Paper Award

    2025

  69. [69]

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

    Peng Wang, Lingjie Liu, Yuan Liu, Christian Theobalt, Taku Komura, and Wenping Wang. NeuS: Learning neural implicit surfaces by volume rendering for multi-view reconstruction. In Advances in Neural Information Processing Systems (NeurIPS), 2021

    2021

  70. [70]

    Efros, and Angjoo Kanazawa

    Qianqian Wang, Yifei Zhang, Aleksander Holynski, Alexei A. Efros, and Angjoo Kanazawa. Continuous 3D perception model with persistent state. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 10510–10522, 2025

    2025

  71. [71]

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

    Ruicheng Wang, Sicheng Xu, Cassie Dai, Jianfeng Xiang, Yu Deng, Xin Tong, and Jiaolong Yang. MoGe: Unlocking accurate monocular geometry estimation for open-domain images with optimal training supervision. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 5261–5271, 2025. 15

    2025

  72. [72]

    MoGe-2: Accurate monocular geometry with metric scale and sharp details

    Ruicheng Wang, Sicheng Xu, Yue Dong, Yu Deng, Jianfeng Xiang, Zelong Lv, Guangzhong Sun, Xin Tong, and Jiaolong Yang. MoGe-2: Accurate monocular geometry with metric scale and sharp details. InAdvances in Neural Information Processing Systems (NeurIPS), 2025

    2025

  73. [73]

    DUSt3R: Geometric 3D vision made easy

    Shuzhe Wang, Vincent Leroy, Yohann Cabon, Boris Chidlovskii, and Jérôme Revaud. DUSt3R: Geometric 3D vision made easy. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024

    2024

  74. [74]

    π3: 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. π3: Permutation-equivariant visual geometry learning. InInternational Conference on Learning Representations (ICLR), 2026. arXiv:2507.13347

    Pith/arXiv arXiv 2026

  75. [75]

    Pro- lificDreamer: High-fidelity and diverse text-to-3D generation with variational score distillation

    Zhengyi Wang, Cheng Lu, Yikai Wang, Fan Bao, Chongxuan Li, Hang Su, and Jun Zhu. Pro- lificDreamer: High-fidelity and diverse text-to-3D generation with variational score distillation. InAdvances in Neural Information Processing Systems (NeurIPS), 2023

    2023

  76. [76]

    NeRFbusters: Removing ghostly artifacts from casually captured NeRFs

    Frederik Warburg, Ethan Weber, Matthew Tancik, Aleksander Holynski, and Angjoo Kanazawa. NeRFbusters: Removing ghostly artifacts from casually captured NeRFs. InProceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2023

    2023

  77. [77]

    Any 3D scene is worth 1K tokens: 3D-grounded representation for scene generation at scale.arXiv preprint arXiv:2604.11331, 2025

    Dongxu Wei, Qi Xu, Zhiqi Li, Hangning Zhou, Cong Qiu, Hailong Qin, Mu Yang, Zhaopeng Cui, and Peidong Liu. Any 3D scene is worth 1K tokens: 3D-grounded representation for scene generation at scale.arXiv preprint arXiv:2604.11331, 2025. Project: https://wswdx. github.io/3DRAE/

    Pith/arXiv arXiv 2025

  78. [78]

    Srinivasan, Dor Verbin, Jonathan T

    Rundi Wu, Ben Mildenhall, Philipp Henzler, Keunhong Park, Ruiqi Gao, Daniel Watson, Pratul P. Srinivasan, Dor Verbin, Jonathan T. Barron, Ben Poole, and Aleksander Holynski. ReconFusion: 3D reconstruction with diffusion priors. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 21551–21561, 2024

    2024

  79. [79]

    Habitat-gs: A high-fidelity navigation simulator with dynamic gaussian splatting.arXiv preprint arXiv:2604.12626, 2026

    Ziyuan Xia, Jingyi Xu, Chong Cui, Yuanhong Yu, Jiazhao Zhang, Qingsong Yan, Tao Ni, Junbo Chen, Xiaowei Zhou, Hujun Bao, Ruizhen Hu, and Sida Peng. Habitat-gs: A high-fidelity navigation simulator with dynamic gaussian splatting.arXiv preprint arXiv:2604.12626, 2026

    Pith/arXiv arXiv 2026

  80. [80]

    Structured 3D latents for scalable and versatile 3D generation

    Jianfeng Xiang, Zelong Lv, Sicheng Xu, Yu Deng, Ruicheng Wang, Bowen Zhang, Dong Chen, Xin Tong, and Jiaolong Yang. Structured 3D latents for scalable and versatile 3D generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR),

Showing first 80 references.