Neural operations that rely on neighborhood information are much more
expensive when deployed on point clouds than on grid data due to the irregular
distances between points in a point cloud. In a grid, on the other hand, we can
compute the kernel only once and reuse it for all query positions. As a result,
operations that rely on neighborhood information scale much worse for point
clouds than for grid data, specially for large inputs and large neighborhoods.
In this work, we address the scalability issue of point cloud methods by
tackling its root cause: the irregularity of the data. We propose learnable
gridification as the first step in a point cloud processing pipeline to
transform the point cloud into a compact, regular grid. Thanks to
gridification, subsequent layers can use operations defined on regular grids,
e.g., Conv3D, which scale much better than native point cloud methods. We then
extend gridification to point cloud to point cloud tasks, e.g., segmentation,
by adding a learnable de-gridification step at the end of the point cloud
processing pipeline to map the compact, regular grid back to its original point
cloud form. Through theoretical and empirical analysis, we show that gridified
networks scale better in terms of memory and time than networks directly
applied on raw point cloud data, while being able to achieve competitive
results. Our code is publicly available at
this https URL

通过可学习的网格化处理将点云数据转化为紧凑、规则的网格，解决了点云方法在规模可扩展性上的问题，使得后续层能够使用更好扩展的操作，在时间和内存消耗上表现更佳且能获得有竞争力的结果。