mlsanityData to 7 Oct 2026

GASCN: Graph Attention Shape Completion Network

arXiv 2201.07937 ↗First posted 20 Jan 2022cs.CVcited by 3

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Abstract (arXiv)

Shape completion, the problem of inferring the complete geometry of an object given a partial point cloud, is an important problem in robotics and computer vision. This paper proposes the Graph Attention Shape Completion Network (GASCN), a novel neural network model that solves this problem. This model combines a graph-based model for encoding local point cloud information with an MLP-based architecture for encoding global information. For each completed point, our model infers the normal and extent of the local surface patch which is used to produce dense yet precise shape completions. We report experiments that demonstrate that GASCN outperforms standard shape completion methods on a standard benchmark drawn from the Shapenet dataset.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 20 of its 40 references that are in the map. First posted on arXiv 20 Jan 2022.

Builds on

20 of 40 references.

References · 20

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8 more
  1. 2019DISN: Deep Implicit Surface Network for High-quality Single-view 3D Reconstructioncited by 332
  2. 2017Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphscited by 307
  3. 2020GRNet: Gridding Residual Network for Dense Point Cloud Completioncited by 174
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  5. 2017High-Resolution Shape Completion Using Deep Neural Networks for Global Structure and Local Geometry Inferencecited by 87
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  7. 2019RL-GAN-Net: A Reinforcement Learning Agent Controlled GAN Network for Real-Time Point Cloud Shape Completioncited by 58
  8. 2016High precision grasp pose detection in dense cluttercited by 33

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Cited by year

Citing papers per year: 2022: 2; 2023: 1.

3 papers.

  1. 2022Equivariant Transporter Networkcited by 44
  2. 2022Edge Grasp Network: A Graph-Based SE(3)-invariant Approach to Grasp Detectioncited by 34
  3. 2023Leveraging Symmetries in Pick and Placecited by 18