  Exploiting convolutional neural networks for point cloud processing is quite
challenging, due to the inherent irregular distribution and discrete shape
representation of point clouds. To address these problems, many handcrafted
convolution variants have sprung up in recent years. Though with elaborate
design, these variants could be far from optimal in sufficiently capturing
diverse shapes formed by discrete points. In this paper, we propose
PointSeaConv, i.e., a novel differential convolution search paradigm on point
clouds. It can work in a purely data-driven manner and thus is capable of
auto-creating a group of suitable convolutions for geometric shape modeling. We
also propose a joint optimization framework for simultaneous search of internal
convolution and external architecture, and introduce epsilon-greedy algorithm
to alleviate the effect of discretization error. As a result, PointSeaNet, a
deep network that is sufficient to capture geometric shapes at both convolution
level and architecture level, can be searched out for point cloud processing.
Extensive experiments strongly evidence that our proposed PointSeaNet surpasses
current handcrafted deep models on challenging benchmarks across multiple tasks
with remarkable margins.
