Pith. sign in

REVIEW 4 major objections 6 minor 59 references

VertexRegen: Mesh Generation with Continuous Level of Detail

T0 review · 4 major / 6 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 VertexRegen reformulates mesh generation as reversing edge collapse, so halting generation at any step yields a complete, valid mesh at a coarser level of detail.

desk verdict Clever vertex-split serialization with a genuinely new anytime-generation claim, but the central validity guarantee is under-demonstrated: the decode-time state machine checks too little to ensure manifold, non-degenerate meshes at every prefix. read the letter →

arxiv 2508.09062 v1 pith:CSUKCWGK submitted 2025-08-12 cs.GR cs.CVcs.LG

Xiang Zhang , Yawar Siddiqui , Armen Avetisyan , Chris Xie , Jakob Engel , Henry Howard-Jenkins This is my paper · ORCID
classification cs.GRcs.CVcs.LG
keywords meshgenerationprogressivemeshesvertexsplitlevelofdetailautoregressivetransformeranytime3Dshape
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

VertexRegen claims that mesh generation can be reframed as learning to reverse edge collapse: instead of emitting triangles until a shape is complete, the model first emits a coarse base mesh and then a sequence of vertex splits, each of which adds two triangles and refines the geometry. Because every split is applied to an already-valid mesh, stopping the autoregressive process at any point yields a complete, usable mesh at a coarser level of detail rather than a half-finished object. The paper presents this as anytime generation with continuous level of detail, and reports quality comparable to existing direct mesh generators on standard metrics while beating them when the face budget is small. The central move is to parameterize a progressive mesh—a mesh plus its split records—as a compact token sequence that a standard next-token-prediction transformer can learn.

What carries the argument

The load-bearing object is the vertex-split parameterization built on a progressive-mesh record and a half-edge data structure. In an edge collapse, two adjacent vertices merge and two faces vanish; the inverse, a vertex split, restores the second vertex and the two faces. VertexRegen encodes each split as a token subsequence giving the target vertex v_s, two ring vertices v_l and v_r (with a <nil> token for boundary edges), and the new vertex position v_t. The half-edge traversal starting from the edge (v_l, v_s) and moving around the vertex ring determines which neighboring vertices belong to v_s and which to v_t after the split, removing the ambiguity that would otherwise require a fourth vertex reference. This yields 12 tokens per interior split (10 on boundaries) and reduces average sequence length to 0.73 times that of a one-token-per-coordinate face soup, approaching the theoretical 0.67 limit. The guided-decoding state machine then enforces validity as tokens are generated, making every prefix decode into a manifold-consistent mesh.

What would settle it

Halt many VertexRegen generations at every step and test each intermediate mesh for manifold validity (each edge shared by exactly two faces, no non-manifold vertices) and compare the intermediate to the QEM simplification of the final output at the same face count; also record how often the predicted M0 itself is valid. If a substantial fraction of prefixes are invalid, or if intermediate meshes diverge strongly from simplified versions of the final mesh, the anytime-generation claim fails.

Watch

Extended reading notes

Core claim

The paper's central claim is that the partial-to-complete paradigm of existing autoregressive mesh generators is not intrinsic to the approach. By constructing training data as progressive meshes—simplifying detailed meshes through repeated edge collapses and then recording the inverse vertex splits—VertexRegen trains a transformer to synthesize meshes from coarse to fine. Each predicted split identifies a vertex to split, two neighbors that locate the edge being re-opened, and the position of the new vertex; a half-edge traversal determines the rest of the connectivity, and a guided-decoding state machine rejects geometrically impossible choices. The result is that the k-th step of generation produces a complete mesh M_k, so the sequence M_0, M_1, ..., M_n is a chain of increasingly detailed valid meshes. Experiments on unconditional and shape-conditioned generation support the claim that quality is comparable to state-of-the-art autoregressive mesh generators, with better early-generation behavior when the face count is constrained.

Load-bearing premise

The load-bearing premise is that every prefix of the generated token stream, including the base mesh M0, can be decoded by the guided-decoding state machine into a topologically valid mesh; the paper does not quantify how often that decoding succeeds, and its own ablation shows that without guided decoding invalid splits end generation early.

Editorial extensions

If this is right

  • A single generation run produces a whole family of level-of-detail meshes, so applications such as rendering, streaming, or previewing can halt the process when compute or bandwidth runs out.
  • Under small face-count budgets, coarse intermediate meshes preserve global structure better than truncated outputs of prior methods, shown by improved COV, MMD, and 1-NNA at early steps.
  • Because the sequence length per added detail is small (12 tokens per two faces), the method generates more faces within a fixed context window than naive triangle-soup tokenization.
  • Guided decoding is a required component: without it, invalid splits break the chain and generation ends early, producing on average 211 faces instead of 320 in the ablation.

Reading between the lines

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

  • The anytime property suggests a practical extension the paper does not develop: a single generated token stream could be stored or transmitted incrementally and rendered progressively, since every prefix is a valid mesh.
  • A natural testable extension is to score intermediate meshes with perceptual or semantic metrics rather than point-cloud distances; the paper's metrics may be insensitive to artifacts that appear at very coarse LODs.
  • Because the model learns splits that reverse QEM-ordered collapses from a filtered training set, its full LOD range is probably only as expressive as the diversity of coarse base meshes in that distribution; measuring whether M0 alone is recognizable as a plausible shape would probe this.
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 / 6 minor

Summary. VertexRegen reframes autoregressive mesh generation as the reversal of edge collapses, i.e., the prediction of vertex splits in a progressive-mesh representation. A coarse base mesh M0 is first generated with a MeshXL-style triangle-soup tokenizer, and then a sequence of vertex splits is predicted as coordinate tokens. A half-edge-based state machine decodes each split on the fly, so that any prefix of the generation process corresponds to a mesh Mk at an intermediate level of detail. The paper evaluates unconditional generation against MeshXL, MeshAnything V2, and EdgeRunner on COV, MMD, 1-NNA, and JSD, and reports comparable quality while offering face-count-constrained generation; it also shows qualitative shape-conditioned results.

Significance. If the anytime-validity claim can be substantiated, this is a meaningful advance: it converts the partial-to-complete paradigm of prior autoregressive mesh generators into a coarse-to-fine one, and the half-edge parameterization is a clean, token-efficient way to resolve the ambiguity in vertex-split rings. The progressive-mesh mathematics is standard, the traversal argument in Eqs. (1)-(3) is sound for manifold oriented meshes, and the compression-ratio analysis in Table 3 is informative. The paper also deserves credit for directly comparing with strong baselines and for ablating the guided-decoding mechanism. The load-bearing gap is empirical verification of the validity guarantee, not the core framing.

major comments (4)
  1. [§3.2.1 (Vertex Split Decoding)] The central claim that "each step yields a valid mesh Mk" is not established by the decoding procedure as written. The state machine enforces only that vs is a vertex of Mk, that (vs, vl) and (vs, vr) are edges, and that at most one of vl and vr is nil; it does not check manifoldness, orientability, absence of duplicate or zero-area faces, or non-degeneracy of the predicted vt. Since vt is a raw discretized coordinate, the model can output vt = vs or a point collinear with vl and vr without violating the listed checks, creating zero-area or inverted faces. Moreover, M0 is produced by the MeshXL-style triangle-soup tokenizer with no validity filter described, so the half-edge structure on which the traversal in Eqs. (1)-(3) relies is not guaranteed to be well-defined for generated meshes. Please specify the full set of validity checks, apply them to every intermediate Mk, and report the pass rate; otherwise the anytime guarantee is exactly as strong as an unverified filter.
  2. [§3.2.1 (Vertex Split Tokenization and Decoding)] The mapping from predicted coordinate tokens to the existing vertices vs, vl, and vr is underspecified. The text says that these references are implemented as raw predictions of each vertex to avoid a vocabulary proportional to sequence length, but it does not explain how a predicted quantized coordinate is matched to a vertex identifier in the current mesh, nor how ties or near-duplicate coordinates are resolved. Since the decoding checks require deciding whether vs is a vertex and whether the two incident edges exist, this matching rule must be defined precisely for the state machine to be implementable and for the validity claim to be testable.
  3. [§4.4 (Guided Decoding)] The ablation in Table 4 reports face counts (211 vs. 320) but not the rate at which the decoding state machine accepts, rejects, or repairs predictions, nor the fraction of generated sequences whose every intermediate mesh Mk is topologically valid. Without those numbers, the reader cannot tell whether the anytime property holds in practice or only for a small fraction of samples. Please report the validity pass rate, the distribution of validity over the steps of each sequence, and the failure modes among rejected splits.
  4. [§4.3.1 (Table 1)] Table 1 reports a single evaluation without error bars or multiple seeds. Given that the paper claims "comparable quality" to state-of-the-art methods, reporting at least three seeds or bootstrap confidence intervals for COV, MMD, 1-NNA, and JSD would make the comparison more robust, especially because the differences between methods are small on most metrics.
minor comments (6)
  1. [§3.2.1] The phrase "only one of vl and vr is allowed to be <nil>" is ambiguous; it should say "at most one" because interior vertex splits have two non-nil neighbors and boundary splits have exactly one nil neighbor.
  2. [§3.2.1] The half-edge notation in Eqs. (1)-(3) and Fig. 4, such as H^k_s or H^k_·s, is not defined in the text; please define the indexing convention before using it in the traversal argument.
  3. [§3.2.1] The formal sequence notation "M : [ <bos>, [M0 sequence], <sep>, [vsplit0], ..., [vsplitn-1], <eos>]" is printed with stray comments (#M0 and #vsplits) that make it hard to read; please format the sequence grammar cleanly.
  4. [§4.3.2] Shape-conditioned generation is evaluated only qualitatively in Fig. 9; if conditional generation is to be considered a supported capability, quantitative conditioned metrics such as COV, MMD, and 1-NNA on a held-out split should be reported.
  5. [§4.1] The sentence "We neither used assets from Sketchfab nor obtained any from the Polycam website" is out of place in the dataset paragraph; if it addresses dataset provenance or licensing, it should be integrated into a concise provenance statement.
  6. [Throughout] The term "continuous level of detail" could be qualified: the method offers discrete resolution steps controlled by the number of applied vertex splits; it is continuous in the sense that generation can be halted at any split, not in the sense of a continuous geometric parameter.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the anytime coarse-to-fine property is a structural consequence of the progressive-mesh parameterization, and the empirical claims are evaluated against external baselines with standard point-cloud metrics.

full rationale

VertexRegen's central claim is that halting the generative process at any step yields a valid mesh at a coarser level of detail. This is not a fitted prediction nor a hidden reuse of an input; it is a design consequence of representing a mesh as a progressive mesh (M0 plus a sequence of vertex splits) and decoding splits through a half-edge state machine. The paper explicitly credits Hoppe's progressive meshes, an external and canonical formulation, and the mathematical definitions of edge collapse and vertex split guarantee that applying a prefix of splits to a valid M0 produces a valid mesh. The evaluation is conducted against external baselines (MeshXL, MeshAnything V2, EdgeRunner) using standard point-cloud metrics (COV, MMD, 1-NNA, JSD), and the quality comparisons are not forced: VertexRegen could have underperformed, and in Table 1 it lands in a comparable range rather than a constructed victory. The face-count-constraint experiment in Figure 7 compares against truncated baselines, and the advantage of having complete meshes at early stopping points is an expected consequence of the design, not a circular restatement of an input. The paper contains self-citations (e.g., MeshGPT, on which one author is listed), but that citation is used only for a vertex-ordering convention and is not load-bearing for the progressive-mesh or anytime-generation claim. The ablation in Table 4 shows that guided decoding is necessary for long sequences, which is an honest engineering limitation rather than a circularity. The reader's concern about the unverified topological validity of M0 and of each decoded split is a correctness/robustness risk, not a circularity: the paper may not fully prove the validity guarantee, but it does not derive the guarantee from itself or rename a fitted quantity as a prediction. Overall, the derivation chain is self-contained relative to its stated benchmarks and assumptions.

Assumptions & free parameters 4 free parameters · 5 assumptions · 0 invented entities

The central claim rests on standard progressive-mesh theory and computational topology (manifold assumptions) plus a set of unstated or hand-chosen hyperparameters (top-p, grid resolution, truncation). No new physical or latent entity is postulated; the vertex split record is a compact serialization, not an invented entity. The 'anytime' guarantee is structural, which attests to low circularity but also means the paper's contribution is the application of known reversible simplification to generative modeling.

free parameters (4)
  • top-p sampling probability = 0.95
    Inference hyperparameter chosen by hand (Section 4.2); affects the diversity and validity of generated vertex split sequences and therefore the anytime generation quality.
  • Coordinate quantization grid resolution N = not specified
    Positions are discretized in an N^3 grid following MeshXL (Section 3.2.1); the exact N is not given, and it caps geometric fidelity.
  • Context window truncation rule = not specified
    During pretraining, vertex split subsequences beyond the context window are discarded (Section 4.1); this shapes the learned face count distribution and the practicality of the anytime property.
  • Face count conditioning buckets = 4 buckets over [1,800]
    Used for the baseline comparison in Table 2 (Section 4.3.1); the choice of buckets affects the FCC baseline result, not VertexRegen itself.
assumptions (5)
  • domain assumption Input meshes are manifold and orientable.
    Filtered during dataset construction (Section 4.1); the half-edge traversal and Eqs. (1)-(3) require manifold topology.
  • standard math Edge collapse and vertex split are exact inverses and preserve topological validity.
    Hoppe's progressive mesh theory (Section 3.1); invoked whenever the paper asserts that any prefix of the split sequence is a valid mesh.
  • standard math The half-edge traversal in Eq. (3) correctly assigns neighbor sets to vs and vt for manifold meshes.
    Computational topology result used in Section 3.2.1 to resolve the ring-splitting ambiguity without extra tokens.
  • domain assumption A pre-trained OPT-350M transformer can be adapted to the proposed progressive-mesh token sequence.
    Section 4.2 initializes all models from OPT-350M; this standard transfer assumption is not analyzed in the paper.
  • domain assumption Point-cloud metrics (COV, MMD, 1-NNA, JSD) measure mesh generation quality.
    Section 4.3.1 relies on these metrics, following prior works; they do not directly measure mesh validity or LOD fidelity.

how reviews work

0 comments
Cite this review

Pith. "Pith review of VertexRegen: Mesh Generation with Continuous Level of Detail." pith.science (2026). https://pith.science/paper/CSUKCWGK

@misc{pith2026250809062,
  author       = {Pith},
  title        = {Pith review of: VertexRegen: Mesh Generation with Continuous Level of Detail},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/CSUKCWGK}},
  note         = {Machine review of arXiv:2508.09062}
}
read the original abstract

We introduce VertexRegen, a novel mesh generation framework that enables generation at a continuous level of detail. Existing autoregressive methods generate meshes in a partial-to-complete manner and thus intermediate steps of generation represent incomplete structures. VertexRegen takes inspiration from progressive meshes and reformulates the process as the reversal of edge collapse, i.e. vertex split, learned through a generative model. Experimental results demonstrate that VertexRegen produces meshes of comparable quality to state-of-the-art methods while uniquely offering anytime generation with the flexibility to halt at any step to yield valid meshes with varying levels of detail.

Figures

Figures reproduced from arXiv: 2508.09062 by the authors.

Figure 1
Figure 1. Mesh generation process of VertexRegen (coarse-to-fine process) vs. previous work (partial-to-complete process). Prior work generates meshes face by face, with the step count corresponding to the face count in the figure. In contrast, VertexRegen produces meshes with a continuously increasing level of detail, where each step yields a valid mesh Mk. Abstract We introduce VertexRegen, a novel mesh generation frame￾wor… view at source ↗
Figure 2
Figure 2. Illustration of edge collapse and its inverse operation, [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 4
Figure 4. Illustration of half-edge data structure and the traversal [PITH_FULL_IMAGE:figures/full_fig_p004_4.png] view at source ↗
Figures from the paper (5 more)
Figure 5
Figure 5. Figure 5: Illustration of VertexRegen tokenization. The sequence [PITH_FULL_IMAGE:figures/full_fig_p004_5.png]
Figure 6
Figure 6. Figure 6: Generation process for VertexRegen. M0 represents the predicted initial coarsest mesh, followed by subsequent meshes generated through the predicted vertex split sequence. split is represented by a subsequence of 12 tokens (or 10 tokens when (vs, vt) is a boundary edge…
Figure 7
Figure 7. Figure 7: Unconditional generation under face count constraints. VertexRegen achieves significantly better COV, MMD, and 1-NNA in [PITH_FULL_IMAGE:figures/full_fig_p006_7.png]
Figure 8
Figure 8. Figure 8: Qualitative comparison with state-of-the-art methods. VertexRegen is able to generate meshes with comparable quality to other [PITH_FULL_IMAGE:figures/full_fig_p007_8.png]
Figure 9
Figure 9. Figure 9: Qualitative results on shape-conditioned generation on meshes held out from training. The first column shows the point cloud [PITH_FULL_IMAGE:figures/full_fig_p007_9.png]

Review history (2 revisions) →

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

59 extracted references · 42 canonical work pages

  1. [1]

    Polydiff: Generating 3d polygonal meshes with diffusion models

    Antonio Alliegro, Yawar Siddiqui, Tatiana Tommasi, and Matthias Nießner. Polydiff: Generating 3d polygonal meshes with diffusion models. arXiv preprint arXiv:2312.11417 ,

    arXiv

  2. [2]

    Meshxl: Neural coordinate field for generative 3d foundation models

    Sijin Chen, Xin Chen, Anqi Pang, Xianfang Zeng, Wei Cheng, Yijun Fu, Fukun Yin, Billzb Wang, Jingyi Yu, Gang Yu, et al. Meshxl: Neural coordinate field for generative 3d foundation models. Advances in Neural Information Pro- cessing Systems, 37:97141–97166, 2024. 1, 2, 3, 4, 6

    work page 2024

  3. [3]

    Meshany- thing v2: Artist-created mesh generation with adjacent mesh tokenization

    Yiwen Chen, Yikai Wang, Yihao Luo, Zhengyi Wang, Zilong Chen, Jun Zhu, Chi Zhang, and Guosheng Lin. Meshany- thing v2: Artist-created mesh generation with adjacent mesh tokenization. arXiv preprint arXiv:2408.02555, 2024. 2, 6

    arXiv 2024

  4. [4]

    Meshanything: Artist-created mesh generation with autoregressive transformers

    Yiwen Chen, Tong He, Di Huang, Weicai Ye, Sijin Chen, Ji- axiang Tang, Zhongang Cai, Lei Yang, Gang Yu, Guosheng Lin, and Chi Zhang. Meshanything: Artist-created mesh generation with autoregressive transformers. In The Thir- teenth International Conference on Learning Representa- tions, 2025. 1, 2

    work page 2025

  5. [5]

    Neural progressive meshes

    Yun-Chun Chen, Vladimir Kim, Noam Aigerman, and Alec Jacobson. Neural progressive meshes. In ACM SIGGRAPH 2023 Conference Proceedings, pages 1–9, 2023. 3

    work page 2023

  6. [6]

    Bsp-net: Generating compact meshes via binary space partitioning

    Zhiqin Chen, Andrea Tagliasacchi, and Hao Zhang. Bsp-net: Generating compact meshes via binary space partitioning. In Proceedings of the IEEE/CVF Conference on Computer Vi- sion and Pattern Recognition (CVPR), 2020. 2

    work page 2020

  7. [7]

    Schwing, and Liang-Yan Gui

    Yen-Chi Cheng, Hsin-Ying Lee, Sergey Tulyakov, Alexan- der G. Schwing, and Liang-Yan Gui. Sdfusion: Multimodal 3d shape completion, reconstruction, and generation. InPro- ceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 4456–4465, 2023. 2

    work page 2023

  8. [8]

    Scan2mesh: From un- structured range scans to 3d meshes

    Angela Dai and Matthias Niessner. Scan2mesh: From un- structured range scans to 3d meshes. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019. 2

    work page 2019

Show all 59 references
  1. [9]

    Objaverse-xl: A universe of 10m+ 3d objects

    Matt Deitke, Ruoshi Liu, Matthew Wallingford, Huong Ngo, Oscar Michel, Aditya Kusupati, Alan Fan, Christian Laforte, Vikram V oleti, Samir Yitzhak Gadre, et al. Objaverse-xl: A universe of 10m+ 3d objects. Advances in Neural Informa- tion Processing Systems, 36:35799–35813, 2023. 6

    2023

  2. [10]

    Cgal: the computational ge- ometry algorithms library

    Andreas Fabri and Sylvain Pion. Cgal: the computational ge- ometry algorithms library. In Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geo- graphic Information Systems, page 538–539, New York, NY , USA, 2009. Association for Computing Machinery. 6

    2009

  3. [11]

    Get3d: A generative model of high quality 3d tex- tured shapes learned from images

    Jun Gao, Tianchang Shen, Zian Wang, Wenzheng Chen, Kangxue Yin, Daiqing Li, Or Litany, Zan Gojcic, and Sanja Fidler. Get3d: A generative model of high quality 3d tex- tured shapes learned from images. In Advances In Neural Information Processing Systems, 2022. 1

    2022

  4. [12]

    Heckbert

    Michael Garland and Paul S. Heckbert. Surface simpli- fication using quadric error metrics. In Proceedings of the 24th Annual Conference on Computer Graphics and Interactive Techniques, page 209–216, USA, 1997. ACM Press/Addison-Wesley Publishing Co. 2, 3

    1997

  5. [13]

    Kim, Bryan C

    Thibault Groueix, Matthew Fisher, Vladimir G. Kim, Bryan C. Russell, and Mathieu Aubry. A papier-m ˆach´e ap- proach to learning 3d surface generation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018. 2

    2018

  6. [14]

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

    Antoine Gu ´edon and Vincent Lepetit. Sugar: Surface- aligned gaussian splatting for efficient 3d mesh reconstruc- tion and high-quality mesh rendering. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 5354–5363, 2024. 2

    2024

  7. [15]

    Meshtron: High-fidelity, artist-like 3d mesh generation at scale

    Zekun Hao, David W Romero, Tsung-Yi Lin, and Ming-Yu Liu. Meshtron: High-fidelity, artist-like 3d mesh generation at scale. arXiv preprint arXiv:2412.09548, 2024. 1, 2

    2024 arXiv

  8. [16]

    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. In The Twelfth International Conference on Learning Representations, 2024. 2

    2024

  9. [17]

    Progressive meshes

    Hugues Hoppe. Progressive meshes. In Proceedings of the 23rd Annual Conference on Computer Graphics and Inter- active Techniques, page 99–108, New York, NY , USA, 1996. Association for Computing Machinery. 2, 3

    1996

  10. [18]

    Shap-e: Generat- ing conditional 3d implicit functions

    Heewoo Jun and Alex Nichol. Shap-e: Generat- ing conditional 3d implicit functions. arXiv preprint arXiv:2305.02463, 2023. 2

    2023 arXiv

  11. [19]

    Diffusion- sdf: Text-to-shape via voxelized diffusion

    Muheng Li, Yueqi Duan, Jie Zhou, and Jiwen Lu. Diffusion- sdf: Text-to-shape via voxelized diffusion. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 12642–12651, 2023. 2

    2023

  12. [20]

    Fast and memory efficient polygonal simplification

    Peter Lindstrom and Greg Turk. Fast and memory efficient polygonal simplification. In Proceedings Visualization’98 (Cat. No. 98CB36276), pages 279–286. IEEE, 1998. 2

    1998

  13. [21]

    Visual instruction tuning

    Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee. Visual instruction tuning. InAdvances in Neural Information Processing Systems, pages 34892–34916. Curran Associates, Inc., 2023. 5

    2023

  14. [22]

    Meshformer: High-quality mesh generation with 3d-guided reconstruction model

    Minghua Liu, Chong Zeng, Xinyue Wei, Ruoxi Shi, Linghao Chen, Chao Xu, Mengqi Zhang, Zhaoning Wang, Xiaoshuai Zhang, Isabella Liu, Hongzhi Wu, and Hao Su. Meshformer: High-quality mesh generation with 3d-guided reconstruction model. In Advances in Neural Information Processing...

    2024

  15. [23]

    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. In Proceedings of the IEEE/CVF Conference on Computer Vision...

  16. [24]

    Lorensen and Harvey E

    William E. Lorensen and Harvey E. Cline. Marching cubes: A high resolution 3d surface construction algorithm. In Proceedings of the 14th Annual Conference on Computer Graphics and Interactive Techniques , page 163–169, New York, NY , USA, 1987. Association for Computing Machin- ery. 2

    1987

  17. [25]

    Decoupled weight de- cay regularization

    Ilya Loshchilov and Frank Hutter. Decoupled weight de- cay regularization. In International Conference on Learning Representations, 2019. 6 9

    2019

  18. [26]

    Cohen, Amitabh Varshney, Benjamin Watson, and Robert Huebner

    David Luebke, Martin Reddy, Jonathan D. Cohen, Amitabh Varshney, Benjamin Watson, and Robert Huebner. Level of Detail for 3D Graphics. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 2002. 2

    2002

  19. [27]

    Occupancy networks: Learning 3d reconstruction in function space

    Lars Mescheder, Michael Oechsle, Michael Niemeyer, Se- bastian Nowozin, and Andreas Geiger. Occupancy networks: Learning 3d reconstruction in function space. InProceedings of the IEEE/CVF Conference on Computer Vision and Pat- tern Recognition (CVPR), 2019. 1

    2019

  20. [28]

    Neural gaussian scale-space fields

    Felix Mujkanovic, Ntumba Elie Nsampi, Christian Theobalt, Hans-Peter Seidel, and Thomas Leimk¨uhler. Neural gaussian scale-space fields. ACM Transactions on Graphics (TOG) , 43(4):1–15, 2024. 3

    2024

  21. [29]

    Polygen: An autoregressive generative model of 3d meshes

    Charlie Nash, Yaroslav Ganin, SM Ali Eslami, and Peter Battaglia. Polygen: An autoregressive generative model of 3d meshes. In International conference on machine learning, pages 7220–7229. PMLR, 2020. 2

    2020

  22. [30]

    Point-e: A system for generat- ing 3d point clouds from complex prompts

    Alex Nichol, Heewoo Jun, Prafulla Dhariwal, Pamela Mishkin, and Mark Chen. Point-e: A system for generat- ing 3d point clouds from complex prompts. arXiv preprint arXiv:2212.08751, 2022. 2

    2022 arXiv

  23. [31]

    Deepsdf: Learning con- tinuous signed distance functions for shape representation

    Jeong Joon Park, Peter Florence, Julian Straub, Richard Newcombe, and Steven Lovegrove. Deepsdf: Learning con- tinuous signed distance functions for shape representation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019. 1

    2019

  24. [32]

    Progressive simplicial complexes

    Jovan Popovi ´c and Hugues Hoppe. Progressive simplicial complexes. In Proceedings of the 24th Annual Conference on Computer Graphics and Interactive Techniques , page 217–224, USA, 1997. ACM Press/Addison-Wesley Publish- ing Co. 2

    1997

  25. [33]

    Rossignac

    J. Rossignac. Edgebreaker: connectivity compression for tri- angle meshes. IEEE Transactions on Visualization and Com- puter Graphics, 5(1):47–61, 1999. 6

    1999

  26. [34]

    V oxgraf: Fast 3d-aware image synthe- sis with sparse voxel grids

    Katja Schwarz, Axel Sauer, Michael Niemeyer, Yiyi Liao, and Andreas Geiger. V oxgraf: Fast 3d-aware image synthe- sis with sparse voxel grids. Advances in Neural Information Processing Systems, 35:33999–34011, 2022. 1

    2022

  27. [35]

    Deep marching tetrahedra: a hybrid repre- sentation for high-resolution 3d shape synthesis

    Tianchang Shen, Jun Gao, Kangxue Yin, Ming-Yu Liu, and Sanja Fidler. Deep marching tetrahedra: a hybrid repre- sentation for high-resolution 3d shape synthesis. Advances in Neural Information Processing Systems , 34:6087–6101,

  28. [36]

    Diffusion-based signed distance fields for 3d shape gener- ation

    Jaehyeok Shim, Changwoo Kang, and Kyungdon Joo. Diffusion-based signed distance fields for 3d shape gener- ation. In Proceedings of the IEEE/CVF Conference on Com- puter Vision and Pattern Recognition (CVPR), pages 20887– 20897, 2023. 2

    2023

  29. [37]

    Meshgpt: Generating triangle meshes with decoder-only transformers

    Yawar Siddiqui, Antonio Alliegro, Alexey Artemov, Ta- tiana Tommasi, Daniele Sirigatti, Vladislav Rosov, Angela Dai, and Matthias Nießner. Meshgpt: Generating triangle meshes with decoder-only transformers. In Proceedings of the IEEE/CVF Conference on Computer Vision and Patte...

    2024

  30. [38]

    Meta 3d assetgen: Text-to-mesh generation with high-quality geometry, texture, and pbr materials

    Yawar Siddiqui, Tom Monnier, Filippos Kokkinos, Mahen- dra Kariya, Yanir Kleiman, Emilien Garreau, Oran Gafni, Natalia Neverova, Andrea Vedaldi, Roman Shapovalov, and David Novotny. Meta 3d assetgen: Text-to-mesh generation with high-quality geometry, texture, and pbr material...

    2024

  31. [39]

    Neural geometric level of detail: Real-time rendering with implicit 3d shapes

    Towaki Takikawa, Joey Litalien, Kangxue Yin, Karsten Kreis, Charles Loop, Derek Nowrouzezahrai, Alec Jacobson, Morgan McGuire, and Sanja Fidler. Neural geometric level of detail: Real-time rendering with implicit 3d shapes. In Proceedings of the IEEE/CVF Conference on Computer...

  32. [40]

    Real-time com- pression and streaming of 4d performances

    Danhang Tang, Mingsong Dou, Peter Lincoln, Philip David- son, Kaiwen Guo, Jonathan Taylor, Sean Fanello, Cem Ke- skin, Adarsh Kowdle, Sofien Bouaziz, et al. Real-time com- pression and streaming of 4d performances. ACM Transac- tions on Graphics (TOG), 37(6):1–11, 2018

    2018

  33. [41]

    Chou, Chris- tian Hane, Mingsong Dou, Sean Fanello, Jonathan Taylor, Philip Davidson, Onur G

    Danhang Tang, Saurabh Singh, Philip A. Chou, Chris- tian Hane, Mingsong Dou, Sean Fanello, Jonathan Taylor, Philip Davidson, Onur G. Guleryuz, Yinda Zhang, Shahram Izadi, Andrea Tagliasacchi, Sofien Bouaziz, and Cem Ke- skin. Deep implicit volume compression. In Proceedings of...

    2020

  34. [42]

    Dreamgaussian: Generative gaussian splatting for ef- ficient 3d content creation

    Jiaxiang Tang, Jiawei Ren, Hang Zhou, Ziwei Liu, and Gang Zeng. Dreamgaussian: Generative gaussian splatting for ef- ficient 3d content creation. InThe Twelfth International Con- ference on Learning Representations, 2024. 2

    2024

  35. [43]

    Edgerunner: Auto-regressive auto-encoder for artistic mesh generation

    Jiaxiang Tang, Zhaoshuo Li, Zekun Hao, Xian Liu, Gang Zeng, Ming-Yu Liu, and Qinsheng Zhang. Edgerunner: Auto-regressive auto-encoder for artistic mesh generation. In The Thirteenth International Conference on Learning Repre- sentations, 2025. 1, 2, 6

    2025

  36. [44]

    Lion: Latent point dif- fusion models for 3d shape generation

    Arash Vahdat, Francis Williams, Zan Gojcic, Or Litany, Sanja Fidler, Karsten Kreis, et al. Lion: Latent point dif- fusion models for 3d shape generation. Advances in Neural Information Processing Systems, 35:10021–10039, 2022. 2

    2022

  37. [45]

    Pixel2mesh: Generating 3d mesh models from single rgb images

    Nanyang Wang, Yinda Zhang, Zhuwen Li, Yanwei Fu, Wei Liu, and Yu-Gang Jiang. Pixel2mesh: Generating 3d mesh models from single rgb images. In Proceedings of the Euro- pean conference on computer vision (ECCV) , pages 52–67,

  38. [46]

    Meshlrm: Large reconstruction model for high- quality mesh

    Xinyue Wei, Kai Zhang, Sai Bi, Hao Tan, Fujun Luan, Valentin Deschaintre, Kalyan Sunkavalli, Hao Su, and Zex- iang Xu. Meshlrm: Large reconstruction model for high- quality mesh. arXiv preprint arXiv:2404.12385, 2024. 2

    2024 arXiv

  39. [47]

    Neumanifold: Neural watertight manifold reconstruction with efficient and high- quality rendering support

    Xinyue Wei, Fanbo Xiang, Sai Bi, Anpei Chen, Kalyan Sunkavalli, Zexiang Xu, and Hao Su. Neumanifold: Neural watertight manifold reconstruction with efficient and high- quality rendering support. In Proceedings of the Winter Con- ference on Applications of Computer Vision (WACV...

    2025

  40. [48]

    Topological structures for geometric mod- eling (Boundary representation, manifold, radial edge struc- ture)

    Kevin J Weiler. Topological structures for geometric mod- eling (Boundary representation, manifold, radial edge struc- ture). Rensselaer Polytechnic Institute, 1986. 4

    1986

  41. [49]

    Pivotmesh: Generic 3d mesh generation via pivot ver- tices guidance

    Haohan Weng, Yikai Wang, Tong Zhang, CL Chen, and Jun Zhu. Pivotmesh: Generic 3d mesh generation via pivot ver- tices guidance. arXiv preprint arXiv:2405.16890, 2024. 2 10

    2024 arXiv

  42. [50]

    Philip Chen

    Haohan Weng, Zibo Zhao, Biwen Lei, Xianghui Yang, Jian Liu, Zeqiang Lai, Zhuo Chen, Yuhong Liu, Jie Jiang, Chun- chao Guo, Tong Zhang, Shenghua Gao, and C.L. Philip Chen. Scaling mesh generation via compressive tokeniza- tion. In Proceedings of the IEEE/CVF Conference on Com- ...

    2025

  43. [51]

    Learning a probabilistic latent space of ob- ject shapes via 3d generative-adversarial modeling

    Jiajun Wu, Chengkai Zhang, Tianfan Xue, Bill Freeman, and Josh Tenenbaum. Learning a probabilistic latent space of ob- ject shapes via 3d generative-adversarial modeling. In Ad- vances in Neural Information Processing Systems . Curran Associates, Inc., 2016. 1

    2016

  44. [52]

    Bayesian diffusion models for 3d shape reconstruction

    Haiyang Xu, Yu Lei, Zeyuan Chen, Xiang Zhang, Yue Zhao, Yilin Wang, and Zhuowen Tu. Bayesian diffusion models for 3d shape reconstruction. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 10628–10638, 2024. 2

    2024

  45. [53]

    Instantmesh: Efficient 3d mesh generation from a single image with sparse-view large reconstruction models

    Jiale Xu, Weihao Cheng, Yiming Gao, Xintao Wang, Shenghua Gao, and Ying Shan. Instantmesh: Efficient 3d mesh generation from a single image with sparse-view large reconstruction models. arXiv preprint arXiv:2404.07191 ,

    arXiv

  46. [54]

    Lion: Latent point diffusion models for 3d shape generation

    Xiaohui Zeng, Arash Vahdat, Francis Williams, Zan Gojcic, Or Litany, Sanja Fidler, and Karsten Kreis. Lion: Latent point diffusion models for 3d shape generation. In Advances in Neural Information Processing Systems, 2022. 1

    2022

  47. [55]

    3dshape2vecset: A 3d shape representation for neu- ral fields and generative diffusion models.ACM Transactions on Graphics (TOG), 42(4):1–16, 2023

    Biao Zhang, Jiapeng Tang, Matthias Niessner, and Peter Wonka. 3dshape2vecset: A 3d shape representation for neu- ral fields and generative diffusion models.ACM Transactions on Graphics (TOG), 42(4):1–16, 2023. 2

    2023

  48. [56]

    Opt: Open pre-trained trans- former language models

    Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen, Christopher Dewan, Mona Diab, Xian Li, Xi Victoria Lin, et al. Opt: Open pre-trained trans- former language models. arXiv preprint arXiv:2205.01068,

    arXiv

  49. [57]

    Depr: Depth guided single-view scene reconstruction with instance-level diffusion

    Qingcheng Zhao, Xiang Zhang, Haiyang Xu, Zeyuan Chen, Jianwen Xie, Yuan Gao, and Zhuowen Tu. Depr: Depth guided single-view scene reconstruction with instance-level diffusion. arXiv preprint arXiv:2507.22825, 2025. 2

    2025 arXiv

  50. [58]

    Michelangelo: Conditional 3d shape generation based on shape-image-text aligned latent representation

    Zibo Zhao, Wen Liu, Xin Chen, Xianfang Zeng, Rui Wang, Pei Cheng, BIN FU, Tao Chen, Gang Yu, and Shenghua Gao. Michelangelo: Conditional 3d shape generation based on shape-image-text aligned latent representation. In Advances in Neural Information Processing Systems , pages 73...

    2023

  51. [59]

    3d shape generation and completion through point-voxel diffusion

    Linqi Zhou, Yilun Du, and Jiajun Wu. 3d shape generation and completion through point-voxel diffusion. In Proceed- ings of the IEEE/CVF International Conference on Com- puter Vision (ICCV), pages 5826–5835, 2021. 1, 2 11

    2021

Pith tools

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