Representation-Agnostic Shape FieldsDownload PDF

29 Sept 2021, 00:30 (edited 14 Mar 2022)ICLR 2022 PosterReaders: Everyone
  • Keywords: shape embedding, 3D deep learning, shape classification and segmentation
  • Abstract: 3D shape analysis has been widely explored in the era of deep learning. Numerous models have been developed for various 3D data representation formats, e.g., MeshCNN for meshes, PointNet for point clouds and VoxNet for voxels. In this study, we present Representation-Agnostic Shape Fields (RASF), a generalizable and computation-efficient shape embedding module for 3D deep learning. RASF is implemented with a learnable 3D grid with multiple channels to store local geometry. Based on RASF, shape embeddings for various 3D shape representations (point clouds, meshes and voxels) are retrieved by coordinate indexing. While there are multiple ways to optimize the learnable parameters of RASF, we provide two effective schemes among all in this paper for RASF pre-training: shape reconstruction and normal estimation. Once trained, RASF becomes a plug-and-play performance booster with negligible cost. Extensive experiments on diverse 3D representation formats, networks and applications, validate the universal effectiveness of the proposed RASF. Code and pre-trained models are publicly available\footnote{\url{}}.
  • One-sentence Summary: We propose a generalizable and computation-efficient shape embedding layer for 3D deep learning, named Representation-Agnostic Shape Fields (RASF), to improve performance across different representations, backbones and down-stream tasks
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