mlsanityData to 7 Oct 2026

Adaptive Local Basis Functions for Shape Completion

arXiv 2307.08348 ↗First posted 17 Jul 2023cs.CVcited by 4

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Abstract (arXiv)

In this paper, we focus on the task of 3D shape completion from partial point clouds using deep implicit functions. Existing methods seek to use voxelized basis functions or the ones from a certain family of functions (e.g., Gaussians), which leads to high computational costs or limited shape expressivity. On the contrary, our method employs adaptive local basis functions, which are learned end-to-end and not restricted in certain forms. Based on those basis functions, a local-to-local shape completion framework is presented. Our algorithm learns sparse parameterization with a small number of basis functions while preserving local geometric details during completion. Quantitative and qualitative experiments demonstrate that our method outperforms the state-of-the-art methods in shape completion, detail preservation, generalization to unseen geometries, and computational cost. Code and data are at https://github.com/yinghdb/Adaptive-Local-Basis-Functions.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 49 of its 59 references that are in the map. First posted on arXiv 17 Jul 2023.

Builds on

49 of 59 references.

References · 49

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37 more
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Cited by year

Citing papers per year: 2024: 4.

4 papers.

  1. 2024Explicitly Guided Information Interaction Network for Cross-modal Point Cloud Completioncited by 9
  2. 2024Neural Garment Dynamics via Manifold-Aware Transformerscited by 8
  3. 2024A Lightweight UDF Learning Framework for 3D Reconstruction Based on Local Shape Functionscited by 7
  4. 2024Fast Medical Shape Reconstruction via Meta-learned Implicit Neural Representationscited by 1