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.

翻译：由于点云的内在不规则分布和离散的形状代表了点云,探索用于点云处理的进化神经网络是相当具有挑战性的。为了解决这些问题,近年来出现了许多手工制造的进化变异体。虽然设计周密,但这些变异体在充分捕捉离散点所形成的不同形状方面可能远非最佳。在本文中,我们提议PointSeara Conv, 即对点云的新型差异变异搜索模式。它可以以纯数据驱动的方式工作,从而能够自动产生一组适合几何形形状模型的组合。我们还提议了一个联合优化框架,用于同时搜索内部进化和外部结构,并采用电子化变异算法来减轻离散错误的影响。结果,PointSearnet是一个深度网络,足以捕捉到分流层和结构层次的几何形状。广泛的实验有力地证明,我们提议的PointSearnet超越了当前在具有惊人距离的多重任务基准上的深层模型。