Paper Info
Title
SVGA-Net: Sparse Voxel-Graph Attention Network for 3D Object Detection from Point Clouds.
Abstract
Accurate 3D object detection from point clouds has become a crucial component in autonomous driving. However, the volumetric representations and the projection methods in previous works fail to establish the relationships between the local point sets. In this paper, we propose Sparse Voxel-Graph Attention Network (SVGA-Net), a novel end-to-end trainable network which mainly contains voxel-graph module and sparse-to-dense regression module to achieve comparable 3D detection tasks from raw LIDAR data. Specifically, SVGA-Net constructs the local complete graph within each divided 3D spherical voxel and global KNN graph through all voxels. The local and global graphs serve as the attention mechanism to enhance the extracted features. In addition, the novel sparse-to-dense regression module enhances the 3D box estimation accuracy through feature maps aggregation at different levels. Experiments on KITTI detection benchmark and Waymo Open dataset demonstrate the efficiency of extending the graph representation to 3D object detection and the proposed SVGA-Net can achieve decent detection accuracy.
Year
Venue
Keywords
2022
AAAI Conference on Artificial Intelligence
Computer Vision (CV),Domain(s) Of Application (APP)
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
0
6
Name
Order
Citations
PageRank
He Qingdong100.34
Zhengning Wang201.35
Zeng Hao300.68
Yi Zeng419230.94
Shuaicheng Liu536328.26
B Zeng61374159.35