Density Aware 3D Object Single Stage Detector

Abstract

In 3D object detection, a lot of works are focused on the characteristics of disorder and sparseness of point cloud. However, few of them directly explore the nonuniformity of point cloud which limits the detection precision. To deal with the problem, a method called Density Aware 3D Object Single Stage Detector (DA-3DSSD) is proposed in this paper. A method of defining point density of point cloud and the network processing strategies on the basis of the calculated density are proposed in DA-3DSSD. These strategies include a Density-aware Points Sampling (DPS) strategy and a Center Density Attention (CDA) module. Specifically, DPS adds a density aware factor in farthest points sampling (FPS), which improve the sampling process sensibility to density. DPS can reserve more internal points for sparse objects to gather more context information in the feature extraction process. CDA module models the relationships between center points densities and features, which can stress object features and suppress non-object features. CDA module strengthens the classification ability of the network. In the experiments, the proposed DA-3DSSD is evaluated on KITTI benchmark. Compared with the baseline method, the average precision has a significant improvement which verifies the effectiveness of our methods.