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.
@article{arxiv.2003.05855,
title = {End-to-End Learning Local Multi-view Descriptors for 3D Point Clouds},
author = {Lei Li and Siyu Zhu and Hongbo Fu and Ping Tan and Chiew-Lan Tai},
journal= {arXiv preprint arXiv:2003.05855},
year = {2020}
}