From voxel to point: IoU-guided 3D object detection for point cloud with voxel-to-point decoder
Document Type
Conference Proceeding
Publication Title
MM 2021 - Proceedings of the 29th ACM International Conference on Multimedia
Abstract
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 URLhttps://github.com/jialeli1/From-Voxel-to-Point .
First Page
4622
Last Page
4631
DOI
10.1145/3474085.3475314
Publication Date
10-17-2021
Keywords
3D object, object detection, point clouds, segmentation
Recommended Citation
J. Li, H. Dai, L. Shao and Y. Ding, "From voxel to point: IoU-guided 3D object detection for point cloud with voxel-to-point decoder", in Proceedings of the 29th ACM International Conference on Multimedia, New York, 2021 , pp. 4622–4631. Available: 10.1145/3474085.3475314
Comments
IR Deposit conditions: non-described