TearingNet: Point Cloud Autoencoder to Learn Topology-Friendly Representations

• URL: http://arxiv.org/abs/2006.10187v4
• Date: Sat, 4 Sep 2021 18:02:55 GMT
• Title: TearingNet: Point Cloud Autoencoder to Learn Topology-Friendly Representations
• Authors: Jiahao Pang, Duanshun Li, Dong Tian
• Abstract summary: We propose an autoencoder, TearingNet, which tackles the challenging task of representing point clouds using a fixed-length descriptor. Our TearingNet is characterized by a proposed Tearing network module and a Folding network module interacting with each other iteratively. Experimentation shows the superiority of our proposal in terms of reconstructing point clouds as well as generating more topology-friendly representations than benchmarks.
• Score: 20.318695890515613
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Topology matters. Despite the recent success of point cloud processing with geometric deep learning, it remains arduous to capture the complex topologies of point cloud data with a learning model. Given a point cloud dataset containing objects with various genera, or scenes with multiple objects, we propose an autoencoder, TearingNet, which tackles the challenging task of representing the point clouds using a fixed-length descriptor. Unlike existing works directly deforming predefined primitives of genus zero (e.g., a 2D square patch) to an object-level point cloud, our TearingNet is characterized by a proposed Tearing network module and a Folding network module interacting with each other iteratively. Particularly, the Tearing network module learns the point cloud topology explicitly. By breaking the edges of a primitive graph, it tears the graph into patches or with holes to emulate the topology of a target point cloud, leading to faithful reconstructions. Experimentation shows the superiority of our proposal in terms of reconstructing point clouds as well as generating more topology-friendly representations than benchmarks.

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