MS23D: A 3D Object Detection Method Using Multi-Scale Semantic Feature Points to Construct 3D Feature Layer

• URL: http://arxiv.org/abs/2308.16518v9
• Date: Sat, 10 Aug 2024 05:07:52 GMT
• Title: MS23D: A 3D Object Detection Method Using Multi-Scale Semantic Feature Points to Construct 3D Feature Layer
• Authors: Yongxin Shao, Aihong Tan, Binrui Wang, Tianhong Yan, Zhetao Sun, Yiyang Zhang, Jiaxin Liu,
• Abstract summary: LiDAR point clouds can effectively depict the motion and posture of objects in three-dimensional space. In autonomous driving scenarios, the sparsity and hollowness of point clouds create some difficulties for voxel-based methods. We propose a two-stage 3D object detection framework, called MS23D.
• Score: 4.644319899528183
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: LiDAR point clouds can effectively depict the motion and posture of objects in three-dimensional space. Many studies accomplish the 3D object detection by voxelizing point clouds. However, in autonomous driving scenarios, the sparsity and hollowness of point clouds create some difficulties for voxel-based methods. The sparsity of point clouds makes it challenging to describe the geometric features of objects. The hollowness of point clouds poses difficulties for the aggregation of 3D features. We propose a two-stage 3D object detection framework, called MS23D. (1) We propose a method using voxel feature points from multi-branch to construct the 3D feature layer. Using voxel feature points from different branches, we construct a relatively compact 3D feature layer with rich semantic features. Additionally, we propose a distance-weighted sampling method, reducing the loss of foreground points caused by downsampling and allowing the 3D feature layer to retain more foreground points. (2) In response to the hollowness of point clouds, we predict the offsets between deep-level feature points and the object's centroid, making them as close as possible to the object's centroid. This enables the aggregation of these feature points with abundant semantic features. For feature points from shallow-level, we retain them on the object's surface to describe the geometric features of the object. To validate our approach, we evaluated its effectiveness on both the KITTI and ONCE datasets.

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