Semantics feature sampling for point-based 3D object detection.

Currently, 3D object detection is a research hotspot in the field of computer vision. In this paper, we have observed that the commonly used set abstraction module retains excessive irrelevant background information during downsampling, impacting object detection precision. To address this, we propose a mixed sampling method. During point feature extraction, we integrate semantic features into the sampling process, guiding the set abstraction module to sample foreground points. In order to leverage the high-quality 3D proposals generated in the first stage, we have developed a virtual point pooling module aimed at acquiring the features of these proposals. This module facilitates the capture of more comprehensive and resilient ROI features. Experimental results on the KITTI test set show a 3.51% higher Average Precision (AP) compared to the PointRCNN baseline, particularly for moderately challenging car classes, highlighting the effectiveness of our approach. • Proposes mixed sampling to enhance 3D object detection precision. • Integrates semantic features for focused foreground point sampling. • Introduces a module for robust feature extraction from high-quality 3D proposals. • Achieves 3.51% higher AP than PointRCNN on KITTI test set, proving effectiveness. [ABSTRACT FROM AUTHOR]