mlsanityData to 14 Oct 2026

Gradient Distance Function

arXiv 2410.22422 ↗First posted 29 Oct 2024cs.CVcited by 0

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Abstract (arXiv)

Unsigned Distance Functions (UDFs) can be used to represent non-watertight surfaces in a deep learning framework. However, UDFs tend to be brittle and difficult to learn, in part because the surface is located exactly where the UDF is non-differentiable. In this work, we show that Gradient Distance Functions (GDFs) can remedy this by being differentiable at the surface while still being able to represent open surfaces. This is done by associating to each 3D point a 3D vector whose norm is taken to be the unsigned distance to the surface and whose orientation is taken to be the direction towards the closest surface point. We demonstrate the effectiveness of GDFs on ShapeNet Car, Multi-Garment, and 3D-Scene datasets with both single-shape reconstruction networks or categorical auto-decoders.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 20 of its 27 references that are in the map. First posted on arXiv 29 Oct 2024.

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20 of 27 references.

References · 20

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2015ShapeNet: An Information-Rich 3D Model Repositorycited by 2,897
  3. 2019DeepSDF: Learning Continuous Signed Distance Functions for Shape Representationcited by 2,689
  4. 2018Occupancy Networks: Learning 3D Reconstruction in Function Spacecited by 2,041
  5. 2018Learning Implicit Fields for Generative Shape Modelingcited by 1,060
  6. 2019DISN: Deep Implicit Surface Network for High-quality Single-view 3D Reconstructioncited by 333
  7. 2020Neural Unsigned Distance Fields for Implicit Function Learningcited by 265
  8. 2019Multi-Garment Net: Learning to Dress 3D People from Imagescited by 245
  9. 2019Pix2Pose: Pixel-Wise Coordinate Regression of Objects for 6D Pose Estimationcited by 218
  10. 2021SMPLicit: Topology-aware Generative Model for Clothed Peoplecited by 155
  11. 2021MeshUDF: Fast and Differentiable Meshing of Unsigned Distance Field Networkscited by 76
  12. 2022NeuralUDF: Learning Unsigned Distance Fields for Multi-view Reconstruction of Surfaces with Arbitrary Topologiescited by 63
8 more
  1. 2023NeUDF: Leaning Neural Unsigned Distance Fields with Volume Renderingcited by 41
  2. 2021Deep Implicit Surface Point Prediction Networkscited by 40
  3. 2021Learning Anchored Unsigned Distance Functions with Gradient Direction Alignment for Single-view Garment Reconstructioncited by 36
  4. 2023NeAT: Learning Neural Implicit Surfaces with Arbitrary Topologies from Multi-view Imagescited by 33
  5. 20223PSDF: Three-Pole Signed Distance Function for Learning Surfaces with Arbitrary Topologiescited by 27
  6. 2023Surface Extraction from Neural Unsigned Distance Fieldscited by 17
  7. 2020DUDE: Deep Unsigned Distance Embeddings for Hi-Fidelity Representation of Complex 3D Surfacescited by 16
  8. 2023Neural Vector Fields: Implicit Representation by Explicit Learningcited by 16

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