mlsanityData to 14 Oct 2026

Neural Template: Topology-aware Reconstruction and Disentangled Generation of 3D Meshes

arXiv 2206.04942 ↗First posted 10 Jun 2022cs.CVcited by 34

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Abstract (arXiv)

This paper introduces a novel framework called DTNet for 3D mesh reconstruction and generation via Disentangled Topology. Beyond previous works, we learn a topology-aware neural template specific to each input then deform the template to reconstruct a detailed mesh while preserving the learned topology. One key insight is to decouple the complex mesh reconstruction into two sub-tasks: topology formulation and shape deformation. Thanks to the decoupling, DT-Net implicitly learns a disentangled representation for the topology and shape in the latent space. Hence, it can enable novel disentangled controls for supporting various shape generation applications, e.g., remix the topologies of 3D objects, that are not achievable by previous reconstruction works. Extensive experimental results demonstrate that our method is able to produce high-quality meshes, particularly with diverse topologies, as compared with the state-of-the-art methods.

Key facts. Cited by 34 papers in the map (data through 14 Oct 2026). Builds on 63 of its 83 references that are in the map. First posted on arXiv 10 Jun 2022.

Builds on

63 of 83 references.

References · 63

  1. 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 6,013
  2. 2018Neural Ordinary Differential Equationscited by 3,779
  3. 2015ShapeNet: An Information-Rich 3D Model Repositorycited by 2,897
  4. 2019DeepSDF: Learning Continuous Signed Distance Functions for Shape Representationcited by 2,689
  5. 2018Occupancy Networks: Learning 3D Reconstruction in Function Spacecited by 2,041
  6. 2018Learning Implicit Fields for Generative Shape Modelingcited by 1,060
  7. 2016A Point Set Generation Network for 3D Object Reconstruction from a Single Imagecited by 979
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  9. 2018Multimodal Unsupervised Image-to-Image Translationcited by 795
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  11. 2018Pixel2Mesh: Generating 3D Mesh Models from Single RGB Imagescited by 651
  12. 20163D-R2N2: A Unified Approach for Single and Multi-view 3D Object Reconstructioncited by 588
38 more
  1. 2018PartNet: A Large-scale Benchmark for Fine-grained and Hierarchical Part-level 3D Object Understandingcited by 526
  2. 2019PointFlow: 3D Point Cloud Generation with Continuous Normalizing Flowscited by 488
  3. 2021Neural Geometric Level of Detail: Real-time Rendering with Implicit 3D Shapescited by 426
  4. 2019SAL: Sign Agnostic Learning of Shapes from Raw Datacited by 390
  5. 2016OctNet: Learning Deep 3D Representations at High Resolutionscited by 342
  6. 2019DISN: Deep Implicit Surface Network for High-quality Single-view 3D Reconstructioncited by 333
  7. 2019Convolutional Mesh Regression for Single-Image Human Shape Reconstructioncited by 270
  8. 2019DRIT++: Diverse Image-to-Image Translation via Disentangled Representationscited by 244
  9. 2019Learning Shape Templates with Structured Implicit Functionscited by 242
  10. 2019BSP-Net: Generating Compact Meshes via Binary Space Partitioningcited by 206
  11. 2017Octree Generating Networks: Efficient Convolutional Architectures for High-resolution 3D Outputscited by 189
  12. 2016Learning Shape Abstractions by Assembling Volumetric Primitivescited by 181
  13. 2017Multi-view Supervision for Single-view Reconstruction via Differentiable Ray Consistencycited by 177
  14. 2019CvxNet: Learnable Convex Decompositioncited by 174
  15. 2016Learning a Predictable and Generative Vector Representation for Objectscited by 173
  16. 2019Superquadrics Revisited: Learning 3D Shape Parsing beyond Cuboidscited by 124
  17. 2019PQ-NET: A Generative Part Seq2Seq Network for 3D Shapescited by 119
  18. 2020Deformed Implicit Field: Modeling 3D Shapes with Learned Dense Correspondencecited by 119
  19. 2019SDM-NET: Deep Generative Network for Structured Deformable Meshcited by 113
  20. 2020MeshSDF: Differentiable Iso-Surface Extractioncited by 112
  21. 2017MarrNet: 3D Shape Reconstruction via 2.5D Sketchescited by 108
  22. 2016Perspective Transformer Nets: Learning Single-View 3D Object Reconstruction without 3D Supervisioncited by 106
  23. 2019Deep Mesh Reconstruction from Single RGB Images via Topology Modification Networkscited by 106
  24. 2019Neural Cages for Detail-Preserving 3D Deformationscited by 106
  25. 2021Neural Parts: Learning Expressive 3D Shape Abstractions with Invertible Neural Networkscited by 103
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  28. 2020Deep Implicit Templates for 3D Shape Representationcited by 86
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  33. 2020Unsupervised Shape and Pose Disentanglement for 3D Meshescited by 61
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50 most cited of 63 All 63 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 1; 2023: 14; 2024: 4; 2025: 8; 2026: 7.

34 papers.

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  1. 2023SALAD: Part-Level Latent Diffusion for 3D Shape Generation and Manipulationcited by 66
  2. 2024PartGen: Part-level 3D Generation and Reconstruction with Multi-View Diffusion Modelscited by 62
  3. 2025AutoPartGen: Autogressive 3D Part Generation and Discoverycited by 33
  4. 2022Neural Shape Deformation Priorscited by 27
  5. 2023Neural Wavelet-domain Diffusion for 3D Shape Generation, Inversion, and Manipulationcited by 27
  6. 2025From One to More: Contextual Part Latents for 3D Generationcited by 26
  7. 2023NAP: Neural 3D Articulation Priorcited by 25
  8. 2023SDF4CHD: Generative Modeling of Cardiac Anatomies with Congenital Heart Defectscited by 17
  9. 2025FullPart: Generating each 3D Part at Full Resolutioncited by 17
  10. 2023Aladdin: Zero-Shot Hallucination of Stylized 3D Assets from Abstract Scene Descriptionscited by 11
  11. 2023Creative Birds: Self-Supervised Single-View 3D Style Transfercited by 9
  12. 2025PartSDF: Part-Based Implicit Neural Representation for Composite 3D Shape Parametrization and Optimizationcited by 9
22 more
  1. 2026LaS-Comp: Zero-shot 3D Completion with Latent-Spatial Consistencycited by 9
  2. 2024CNS-Edit: 3D Shape Editing via Coupled Neural Shape Optimizationcited by 8
  3. 2024Self-supervised Learning of Hybrid Part-aware 3D Representations of 2D Gaussians and Superquadricscited by 8
  4. 2024ShapeFusion: A 3D diffusion model for localized shape editingcited by 7
  5. 2023Explorable Mesh Deformation Subspaces from Unstructured Generative Modelscited by 6
  6. 2023Registered and Segmented Deformable Object Reconstruction from a Single View Point Cloudcited by 6
  7. 2026CubePart: An Open-Vocabulary Part-Controllable 3D Generatorcited by 5
  8. 2023Adaptive Local Basis Functions for Shape Completioncited by 4
  9. 2023Point Cloud Part Editing: Segmentation, Generation, Assembly, and Selectioncited by 4
  10. 2023A Review of Deep Learning-Powered Mesh Reconstruction Methodscited by 3
  11. 2023Semantic-Aware Implicit Template Learning via Part Deformation Consistencycited by 3
  12. 2026EPS3D: End-to-End Feed-Forward 3D Panoptic Segmentationcited by 3
  13. 2023Intelligent Generation of Graphical Game Assets: A Conceptual Framework and Systematic Review of the State of the Artcited by 1
  14. 2025Chirpy3D: Part-Aware Multi-View Diffusion for Creative Fine-Grained Object Generationcited by 1
  15. 2025Relational Anatomical Supervision for Accurate 3D Multi-Chamber Cardiac Mesh Reconstructioncited by 1
  16. 2026Quartet of Diffusions: Structure-Aware Point Cloud Generation through Part and Symmetry Guidancecited by 1
  17. 2026BoxSplitGen: A Generative Model for 3D Part Bounding Boxes in Varying Granularitycited by 1
  18. 2023Pix2Repair: Implicit Shape Restoration from Imagescited by 0
  19. 2025WonderVerse: Extendable 3D Scene Generation with Video Generative Modelscited by 0
  20. 2025HPR3D: Hierarchical Proxy Representation for High-Fidelity 3D Reconstruction and Controllable Editingcited by 0
  21. 2026D-Prism: Differentiable Primitives for Structured Dynamic Modelingcited by 0
  22. 2026CNS-Edit++: Category-Agnostic 3D Editing with Coupled Neural Shape Representationcited by 0