  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.
