Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
10-2024
Abstract
Single-stage object detection from 3D point clouds in autonomous driving faces significant challenges, particularly in accurately detecting small objects. To address this issue, we propose a novel method called Point-Voxel dual-branch feature extraction with Partitioned point cloud sampling for anchor-free Single-Stage Detection of 3D objects (PVP-SSD). The network comprises two branches: a point branch and a voxel branch. In the point branch, a partitioned point cloud sampling strategy leverages axial features to divide the point cloud. Then, it assigns different sampling weights to various segments to enhance the sampling accuracy. Additionally, a local feature enhancement module explicitly calculates the correlation between key points and query points, improving the extraction of local features. In the voxel branch, we use 3D sparse convolution to extract instance structural features efficiently. The point-voxel dual-branch fusion dynamically integrates instance features extracted from both branches using a self-attention mechanism, which contains not only the category information of the detected object but also the spatial dimensions and heading angle. Consequently, PVP-SSD achieves a certain balance between preserving detailed information and maintaining structural integrity. Experimental results on the KITTI and ONCE datasets demonstrate that PVP-SSD excels in multi-category small 3D object detection.
Keywords
3D object detection, autonomous driving, partitioned point cloud, self-attention mechanism
Discipline
Graphics and Human Computer Interfaces
Research Areas
Intelligent Systems and Optimization
Areas of Excellence
Digital transformation
Publication
Proceedings of the 33rd Pacific Conference on Computer Graphics and Applications (Pacific Graphics 2025), Taipei, Taiwan, 2025 October 14-17
First Page
1
Last Page
12
ISBN
9783038682509
Identifier
10.2312/pg.20241279
Publisher
The Eurographics Association
City or Country
Huangshan, China
Citation
WU, Xinlin; TIAN, Yibin; PAN, Yin; ZHANG, Zhiyuan; WU, Xuesong; WANG, Ruisheng; and ZENG, Zhi.
PVP-SSD: Point-voxel fusion with partitioned point cloud sampling for anchor-free single-stage small 3D object detection. (2024). Proceedings of the 33rd Pacific Conference on Computer Graphics and Applications (Pacific Graphics 2025), Taipei, Taiwan, 2025 October 14-17. 1-12.
Available at: https://ink.library.smu.edu.sg/sis_research/10166
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.2312/pg.20241279