Feature Based Sampling: A Fast and Robust Sampling Method for Tasks Using 3D Point Cloud

Abstract

Point cloud data sets are frequently used in machines to sense the real world because sensors such as LIDAR are readily available to be used in many applications including autonomous cars and drones. PointNet and PointNet++ are widely used point-wise embedding methods for interpreting Point clouds. However, even for recent models based on PointNet, real-time inference is still challenging. The solution to a faster inference is sampling, where, sampling is a method to reduce the number of points that is computed in the next module. Furthest Point Sampling (FPS) is widely used, but disadvantage is that it is slow and it is difficult to select critical points. In this paper, we introduce Feature-Based Sampling (FBS), a novel sampling method that applies the attention technique. The results show a significant speedup of the training time and inference time while the accuracy is similar to previous methods. Further experiments demonstrate that the proposed method is better suited to preserve critical points or discard unimportant points.