2021/08/08 by Jiale Li, Hang Dai, Li, Jiale +5 · 1 citation
Computer Science · Engineering · #3D Shape Modeling and Analysis #Advanced Neural Network Applications #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Robotics and Sensor-Based Localization #cs.CV
paper · pdf · doi:10.48550/arxiv.2108.03648
This is a pre-print of our paper published in proceedings of the 29th ACM International Conference on Multimedia (MM'21)
arxiv created 2021/08/08 · openalex publication_date 2021/08/08 · arxiv updated 2021/08/10 · openalex created_date 2021/08/16 · openalex updated_date 2026/07/28
In this paper, we present an Intersection-over-Union (IoU) guided two-stage 3D object detector with a voxel-to-point decoder. To preserve the necessary information from all raw points and maintain the high box recall in voxel based Region Proposal Network (RPN), we propose a residual voxel-to-point decoder to extract the point features in addition to the map-view features from the voxel based RPN. We use a 3D Region of Interest (RoI) alignment to crop and align the features with the proposal boxes for accurately perceiving the object position. The RoI-Aligned features are finally aggregated with the corner geometry embeddings that can provide the potentially missing corner information in the box refinement stage. We propose a simple and efficient method to align the estimated IoUs to the refined proposal boxes as a more relevant localization confidence. The comprehensive experiments on KITTI and Waymo Open Dataset demonstrate that our method achieves significant improvements with novel architectures against the existing methods. The code is available on Github URL\footnote\urlhttps://github.com/jialeli1/From-Voxel-to-Point.