Complementary Features with Reasonable Receptive Field for Road Scene 3D Object Detection

Abstract

Accurate and efficient 3D object detection is of great importance for autonomous driving and robot perception. There are two problems in lidar based 3D object detection networks. (1) Semantic information (e.g., class label of each point) and spatial details are not fully explored for feature extraction. (2) The variance of object sizes represented by point cloud is much smaller than those represented by 2D images. But existing methods do not make use of this property and the receptive field sizes generally mismatch the physical sizes of road scene objects. Based on these two aspects, we propose Complementary Features with Reasonable receptive field networks (CFRNet). CFRNet first exploits Complementary Feature Extractor to learn semantic and positional features, then utilizes a RPN (Region Proposal Networks) with reasonable receptive field to collect correlated context in road scene. Experimental results on KITTI benchmark show the effectiveness of our method. Moreover, our method achieves state-of-the-art performance at a high inference speed.