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arXiv · 2003.05855

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

Lei Li·Siyu Zhu·Hongbo Fu·Ping Tan·Chiew-Lan Tai
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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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Lei Li, Siyu Zhu, Hongbo Fu, Ping Tan, Chiew-Lan Tai. 2020-03-12. End-to-End Learning Local Multi-view Descriptors for 3D Point Clouds. https://arxiv.org/abs/2003.05855

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