计算机辅助设计与图形学学报

点云粒子滤波贝叶斯重构

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冯肖维 , 赵一平 , 陈信钰 , 王锡淮

计算机辅助设计与图形学学报 | 三维图形计算 2025,37(12): 2088-2098

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计算机辅助设计与图形学学报 |三维图形计算 2025 , 37 (12) : 2088 -2098

点云粒子滤波贝叶斯重构

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冯肖维, 赵一平, 陈信钰, 王锡淮

作者信息

上海海事大学电气自动化系　上海　201306

通讯作者:

（xwfeng@shmtu.edu.cn）

作者简介:

冯肖维（1982—），男，博士，讲师，硕士生导师，主要研究方向为计算机图形学、机器视觉、机器人导航

赵一平（1999—），男，硕士研究生，主要研究方向为计算机图形学

陈信钰（1999—），男，硕士研究生，主要研究方向为计算机图形学

王锡淮（1961—），男，博士，教授，主要研究方向为信号处理、系统建模与优化.

Point Cloud Bayesian Reconstruction Based on Particle Filtering

Xiaowei Feng, Yiping Zhao, Xinyu Chen, Xihuai Wang

Affiliations

Department of Electrical Automation, Shanghai Maritime University, Shanghai　201306

出版时间: 2025-12-20 doi: 10.3724/SP.J.1089.2023-00810

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摘要

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针对三维点云重构过程中由于过度平滑和锐化造成的特征失真问题，提出一种具有特征保持的概率重构算法.首先利用空间主成分分析进行点云局部曲率的低秩估计，辨识尖锐特征，并根据曲率对法向场进行自适应优化，以保证尖锐特征恢复时能够获得准确的法向约束；然后依据贝叶斯统计推论建立点云最大后验概率重构模型，并设计了粒子滤波器对各点位置的后验概率分布进行序贯蒙特卡罗模拟；为了提高算法的收敛速度，根据局部噪声强弱自适应地调整粒子重采样范围；最后针对点云不同区域分别使用法向与球体重采样完成多假设检验重构，解决特征退化和边缘不规整的问题. 与多种典型算法的对比实验结果表明，所提算法在主观视觉效果、重构精度和效率方面均有改善，重构精度与时效分别平均提高46.84%和33.34%；该算法在抑制噪声的同时，能够有效地恢复原有特征，提高了点云质量.

关键词

点云重构 / 贝叶斯估计 / 粒子滤波 / 法向估计 / 多假设检验

Abstract

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To solve the problem of feature distortion induced by excessive smoothing or sharpening during the reconstruction of 3D point cloud, a feature-preserving probabilistic reconstruction method is proposed. First, low-rank estimation of local curvature of point cloud is realized by space-based principal component analysis in order to better locate sharp features, and the normal field of point cloud is adaptively optimized based on curvature which guarantees accurate normal constraints during sharp feature recovery. Then, the probability reconstruction model of point cloud is established according to Bayesian statistic inference. And the posterior probability distribution of each point position is simulated by sequential Monte Carlo method within particle filtering framework. In order to improve the convergence, the resampling range is adjusted according to the local noise. Finally, the Bayesian reconstruction of point cloud is achieved using resampling with normal and spherical constraints for different regions which takes the advantage of multiple hypothesis testing, and the problems of feature degradation and irregular edges are solved. Compared with several state-of-the-art methods, the experimental results show that the proposed algorithm has improved subjective visual effects, reconstruction accuracy, and efficiency, and the reconstruction error is reduced by 46.84% while the running time is decreased by 33.34% on average. The proposed method can effectively restore original features while suppressing noise, which improve the quality of point cloud.

Key words

point cloud reconstruction / Bayesian estimation / particle filtering / normal estimation / multiple hypothesis testing

引用本文

冯肖维, 赵一平, 陈信钰, 王锡淮. 点云粒子滤波贝叶斯重构. 计算机辅助设计与图形学学报, 2025 , 37 (12) : 2088 -2098 . DOI: 10.3724/SP.J.1089.2023-00810

Xiaowei Feng, Yiping Zhao, Xinyu Chen, Xihuai Wang. Point Cloud Bayesian Reconstruction Based on Particle Filtering[J]. Journal of Computer-Aided Design & Computer Graphics, 2025 , 37 (12) : 2088 -2098 . DOI: 10.3724/SP.J.1089.2023-00810

基金

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上海市科委重点项目(Z20218044)

参考文献

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2025年第37卷第12期

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文章信息

doi: 10.3724/SP.J.1089.2023-00810

接收时间：2023-12-19
首发时间：2026-10-10
出版时间：2025-12-20

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出版历史

收稿日期：2023-12-19
修回日期：2024-09-05

基金

上海市科委重点项目(Z20218044)

作者信息

上海海事大学电气自动化系　上海　201306

通讯作者:

（xwfeng@shmtu.edu.cn）

参考文献

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https://castjournals.cast.org.cn/joweb/jsjfzsj/CN/10.3724/SP.J.1089.2023-00810

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2种不同金属材料的力学参数

科 Family	属数 Number of genus	种数 Number of species	占总种数比例 Percentage of total species (%)	属 Genus	种数 Number of species	占总种数比例 Percentage of total species (%)
鹅膏菌科Amanitaceae	2	11	5.26	鹅膏菌属 Amanita	10	4.78
小菇科 Mycenaceae	2	12	5.74	丝盖伞属 Inocybe	5	2.39
多孔菌科 Polyporaceae	8	14	6.70	蜡蘑属 Laccaria	5	2.39
红菇科 Russulaceae	3	23	11.00	小皮伞属 Marasmius	6	2.87
				小菇属 Mycena	11	5.26
				光柄菇属 Pluteus	5	2.39
				红菇属 Russula	17	8.13
				栓菌属 Trametes	5	2.39

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