End-to-End Learning Local Multi-view Descriptors for 3D Point Clouds

• URL: http://arxiv.org/abs/2003.05855v2
• Date: Mon, 16 Mar 2020 14:32:05 GMT
• Title: End-to-End Learning Local Multi-view Descriptors for 3D Point Clouds
• Authors: Lei Li, Siyu Zhu, Hongbo Fu, Ping Tan, Chiew-Lan Tai
• Abstract summary: We propose an end-to-end framework to learn local multi-view descriptors for 3D point clouds. Our method outperforms existing local descriptors both quantitatively and qualitatively.
• Score: 67.27510166559563
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this work, we propose an end-to-end framework to learn local multi-view descriptors for 3D point clouds. To adopt a similar multi-view representation, existing studies use hand-crafted viewpoints for rendering in a preprocessing stage, which is detached from the subsequent descriptor learning stage. In our framework, we integrate the multi-view rendering into neural networks by using a differentiable renderer, which allows the viewpoints to be optimizable parameters for capturing more informative local context of interest points. To obtain discriminative descriptors, we also design a soft-view pooling module to attentively fuse convolutional features across views. Extensive experiments on existing 3D registration benchmarks show that our method outperforms existing local descriptors both quantitatively and qualitatively.

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