MCFP: A multi-target 3D perception method with weak dependence on 2D detectors

Abstract

3D perception for multi-target in complex traffic environments plays an important role in autonomous driving. Nowadays, some mainstream models use 2D detectors to provide auxiliary information for 3D perception. But it is a challenge that the accuracy of 2D detectors has a sensitive impact on the final results. In this paper, we propose MCFP, a multi-target 3D perception model with weak dependence on 2D detectors, to improve the 3D perception performance. We use the point cloud expansion as the main idea to provide more information about instance objects for model learning. Firstly, the centroid awareness strategy is integrated into the front-end part for downsampling and feature extraction to reduce the loss of object-related information. Secondly, the ball query operation with adaptive radius is designed to obtain more complete object information. Experiments show that our model outperforms baseline models in normal environments, and achieves detection performances close to those of SOTA models in extreme environments, which implies that our model has a strong ability to obtain semantic information and hence enhancing the perception ability. The code and data will be made available at https://github.com/MrSakasky/MCFP.