An Anchor-Free 3D Object Detection Approach Based on Hierarchical Pillars

Object detection in the 2D domain is well developed owing to the wide application of CMOS image sensors and the great success of deep learning technologies in recent years. However, under circumstances such as autonomous driving, the variation of weather conditions and light conditions makes it impossible to perform reliable detection using regular 2D image sensors. 3D data generated by a Lidar or Radar is more robust to such environments, hence serving as an essential complement to 2D data in such scenarios. Well-established anchor-based detectors in the 2D domain suffer from time-consuming anchor configuration and cannot be exploited directly to process 3D data. This paper proposes an anchor-free network that encodes the raw point cloud into a hierarchical pillar representation to locate objects. Without predefined anchors and NMS postprocessing, our method directly predicts the center points and box properties to accomplish the detection task efficiently. In addition, a PCA-based initialization for the convolutional kernel is proposed to accelerate the training process. Experiments are implemented on the KITTI benchmark, and our method can achieve competitive performance with other anchor-based methods. Comprehensive ablation studies further verify the validity and rationality of each part of the proposed method.