点云目标检测残差投票网络

杨积升; 章云; 李东

doi:10.12052/gdutxb.200176

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点云目标检测残差投票网络

综合研究 | 更新时间：2026-05-27

- 点云目标检测残差投票网络
- A Residual Neural Network with Voting for 3D Object Detection in Point Clouds
- 广东工业大学学报 2022年39卷第1期页码：56-62
- 作者机构：
  
  广东工业大学自动化学院,广东,广州,510006
- 作者简介：
  
  杨积升(1995–)，男，硕士研究生，主要研究方向为点云处理、模式识别
  李东(1983–)，男，副教授，博士，硕士生导师，主要研究方向为模式识别、机器学习、人脸识别和机器视觉，E-mail：dong.li@gdut.edu.cn
- 基金信息：
  
  国家自然科学基金资助项目(61503084);广东省自然科学基金资助项目(2021A1515011867)
- DOI：10.12052/gdutxb.200176
  中图分类号： TP391.4;TP181
- 纸质出版：2022
- 稿件说明：
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杨积升, 章云, 李东. 点云目标检测残差投票网络[J]. 广东工业大学学报, 2022, 39(1): 56-62.

Yang Ji-sheng, Zhang Yun, Li Dong. A Residual Neural Network with Voting for 3D Object Detection in Point Clouds[J]. 2022, 39(1): 56-62.
杨积升, 章云, 李东. 点云目标检测残差投票网络[J]. 广东工业大学学报, 2022, 39(1): 56-62. DOI： 10.12052/gdutxb.200176.

Yang Ji-sheng, Zhang Yun, Li Dong. A Residual Neural Network with Voting for 3D Object Detection in Point Clouds[J]. 2022, 39(1): 56-62. DOI： 10.12052/gdutxb.200176.

摘要

高精度的三维目标检测是实现物体感知的关键技术

对自动驾驶、机器人控制等应用的落地具有重要意义。为提高三维目标检测的精度

对算法VoteNet改进

提出了一种基于残差网络的端到端的高精度三维点云目标检测网络ResVoteNet。具体来说

设计了适用于点云数据的残差网络骨架

提出了残差特征提取模块以及残差上采样模块

并集成进VoteNet框架。残差网络结构的引入增强了网络对点云数据的特征提取和学习能力

并且提高了模型的鲁棒性。该算法在公开的大规模点云数据集SCANNET和SUN-RGBD上进行实验

平均检测精度mAP分别达到61.1%和59.9%

超越了当前最先进水平的其他算法。

Abstract

High-precision 3D object detection is a key technology to realize object perception

which is of great significance to the implementation of applications such as automatic driving and robot control. In order to improve the accuracy of 3D object detection

the algorithm VoteNet is improved

and an end-to-end high-precision 3D point cloud target detection network based on residual network

ResVoteNet is proposed. Specifically

a residual network skeleton suitable for point cloud data is designed

and a residual feature extraction module and a residual up-sampling module are proposed and integrated into the VoteNet framework. The introduction of the residual network structure enhances the network's feature extraction and learning capabilities for point cloud data

and improves the robustness of the model. The algorithm is tested on the publicly available large-scale point cloud data sets SCANNET and SUN-RGBD

and the average detection accuracy mAP has reached 61.1% and 59.9%

respectively

surpassing other current state-of-the-art algorithms.

关键词

Keywords

references

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