PointMax: Self-Boosted Local Sampling for 3D Point Cloud Analysis

Local sampling plays a key role in modeling 3D point clouds. Due to the disordered and unstructured nature of point cloud data, conventional 3D deep models such as PointNet++ and its variants usually employ random or fixed rules to sample local neighborhoods, leading to considerable redundancy in the feature aggregation process. In this paper, we propose a self-supervised method for learning to adaptively select effective neighbors. Firstly, we observe that only a part of sampled points contributes to the aggregated features after the max-pooling operation in existing point cloud models. Then, based on this observation, we propose a simple and task-oriented metric to evaluate the sampling efficiency by measuring the effective neighbors in the feature aggregation process. The metric is also used to supervise a lightweight neighborhood scoring module (NSM), which is designed to efficiently select effective neighboring points from a wider range of neighbors to reduce the computational cost and keep the performance superior. To further improve the performance, we introduce Neighborhood Attention in the feature aggregation process according to the importance score of neighborhood points predicted by NSM. Experimental results show that our method is simple and efficient, and can be applied to most tasks and models to reduce the computational cost and keep the performance superiority. Our code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/sunshuofeng/PointMax_Code</uri>