mlsanityData to 14 Oct 2026

LoopDraw: a Loop-Based Autoregressive Model for Shape Synthesis and Editing

arXiv 2212.04981 ↗First posted 9 Dec 2022cs.GR cs.CVcited by 1

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Abstract (arXiv)

There is no settled universal 3D representation for geometry with many alternatives such as point clouds, meshes, implicit functions, and voxels to name a few. In this work, we present a new, compelling alternative for representing shapes using a sequence of cross-sectional closed loops. The loops across all planes form an organizational hierarchy which we leverage for autoregressive shape synthesis and editing. Loops are a non-local description of the underlying shape, as simple loop manipulations (such as shifts) result in significant structural changes to the geometry. This is in contrast to manipulating local primitives such as points in a point cloud or a triangle in a triangle mesh. We further demonstrate that loops are intuitive and natural primitive for analyzing and editing shapes, both computationally and for users.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 24 of its 54 references that are in the map. First posted on arXiv 9 Dec 2022.

Builds on

24 of 54 references.

References · 24

  1. 2017Attention Is All You Needcited by 60,361
  2. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,248
  3. 2022DreamFusion: Text-to-3D using 2D Diffusioncited by 3,025
  4. 2015ShapeNet: An Information-Rich 3D Model Repositorycited by 2,897
  5. 2019DeepSDF: Learning Continuous Signed Distance Functions for Shape Representationcited by 2,689
  6. 2018Occupancy Networks: Learning 3D Reconstruction in Function Spacecited by 2,041
  7. 2018Learning Implicit Fields for Generative Shape Modelingcited by 1,060
  8. 2022Score Jacobian Chaining: Lifting Pretrained 2D Diffusion Models for 3D Generationcited by 597
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  11. 2021DeepCAD: A Deep Generative Network for Computer-Aided Design Modelscited by 271
  12. 2020PolyGen: An Autoregressive Generative Model of 3D Meshescited by 269
12 more
  1. 2020Learning Gradient Fields for Shape Generationcited by 245
  2. 20223DILG: Irregular Latent Grids for 3D Generative Modelingcited by 93
  3. 2020DualSDF: Semantic Shape Manipulation using a Two-Level Representationcited by 82
  4. 2020Neural Mesh Flow: 3D Manifold Mesh Generation via Diffeomorphic Flowscited by 61
  5. 2020Deep Geometric Texture Synthesiscited by 51
  6. 2020Neural Subdivisioncited by 49
  7. 2020Adversarial Generation of Continuous Implicit Shape Representationscited by 46
  8. 2022CLIP-Sculptor: Zero-Shot Generation of High-Fidelity and Diverse Shapes from Natural Languagecited by 46
  9. 2021Joint Learning of 3D Shape Retrieval and Deformationcited by 42
  10. 2020DSG-Net: Learning Disentangled Structure and Geometry for 3D Shape Generationcited by 31
  11. 2021Orienting Point Clouds with Dipole Propagationcited by 31
  12. 2022TetGAN: A Convolutional Neural Network for Tetrahedral Mesh Generationcited by 8

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Citing papers per year: 2025: 1.

1 paper.

  1. 2025Eval3D: Interpretable and Fine-grained Evaluation for 3D Generationcited by 16