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

Lei Li, Siyu Zhu, Hongbo Fu, Ping Tan, Chiew-Lan Tai; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. 1919-1928

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.

Related Material


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[bibtex]
@InProceedings{Li_2020_CVPR,
author = {Li, Lei and Zhu, Siyu and Fu, Hongbo and Tan, Ping and Tai, Chiew-Lan},
title = {End-to-End Learning Local Multi-View Descriptors for 3D Point Clouds},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2020}
}