From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network

Citations Over TimeTop 1% of 2020 papers

Abstract

3D object detection from LiDAR point cloud is a challenging problem in 3D scene understanding and has many practical applications. In this paper, we extend our preliminary work PointRCNN to a novel and strong point-cloud-based 3D object detection framework, the part-aware and aggregation neural network (Part- A2 net). The whole framework consists of the part-aware stage and the part-aggregation stage. First, the part-aware stage for the first time fully utilizes free-of-charge part supervisions derived from 3D ground-truth boxes to simultaneously predict high quality 3D proposals and accurate intra-object part locations. The predicted intra-object part locations within the same proposal are grouped by our new-designed RoI-aware point cloud pooling module, which results in an effective representation to encode the geometry-specific features of each 3D proposal. Then the part-aggregation stage learns to re-score the box and refine the box location by exploring the spatial relationship of the pooled intra-object part locations. Extensive experiments are conducted to demonstrate the performance improvements from each component of our proposed framework. Our Part- A2 net outperforms all existing 3D detection methods and achieves new state-of-the-art on KITTI 3D object detection dataset by utilizing only the LiDAR point cloud data.

Related Papers

• → Intelligent Detection of Missing and Unattended Objects in Complex Scene of Surveillance Videos(2012)10 cited
• → 3D Object Detection from LiDAR Data using Distance Dependent Feature Extraction(2020)5 cited
• → BEV Approach Based Efficient Object Detection using YoloV4 for LiDAR Point Cloud(2023)1 cited
• → Towards Object Detection from Motion(2019)1 cited
• → Object extraction from stereo vision using continuity of disparity map(2012)