We present a novel spatial hashing based data structure to facilitate 3D
shape analysis using convolutional neural networks (CNNs). Our method well
utilizes the sparse occupancy of 3D shape boundary and builds hierarchical hash
tables for an input model under different resolutions. Based on this data
structure, we design two efficient GPU algorithms namely hash2col and col2hash
so that the CNN operations like convolution and pooling can be efficiently
parallelized. The spatial hashing is nearly minimal, and our data structure is
almost of the same size as the raw input. Compared with state-of-the-art
octree-based methods, our data structure significantly reduces the memory
footprint during the CNN training. As the input geometry features are more
compactly packed, CNN operations also run faster with our data structure. The
experiment shows that, under the same network structure, our method yields
comparable or better benchmarks compared to the state-of-the-art while it has
only one-third memory consumption. Such superior memory performance allows the
CNN to handle high-resolution shape analysis.

本文介绍了一种基于空间哈希的数据结构，该结构可以方便使用卷积神经网络进行三维形状分析。相比现有的基于边界的方法，使用该数据结构可以极大地降低内存消耗，提高 CNN 的运行速度，同时保证了分析结果的准确性。