首页|点云质量评价研究综述

点云质量评价研究综述

扫码查看
点云作为3D数据的重要形式,在智能驾驶等领域广泛应用.在点云压缩过程中,失真现象难以避免,如何准确评估压缩后点云数据的质量已成为该领域的研究重点.对点云质量评价研究进行了探讨.首先对比了几种典型的点云数据集;其次从传统算法和深度学习算法两个方面对点云客观质量评价模型进行了讨论;最后对点云质量评价算法面临的机遇和挑战进行总结和展望.该研究可以为点云数据的优化和压缩算法的改进提供指导和参考.
Overview of point cloud quality evaluation research
As an important form of 3D data,point cloud has been widely used in intelligent driving and other fields.In the pro-cess of point cloud compression,distortion is inevitable.How to accurately evaluate the quality of point cloud data after compres-sion has become the research focus of this field.This paper discusses the research of point cloud quality evaluation.First,several typical point cloud data sets are compared.Secondly,the objective quality evaluation model of point cloud is discussed from the as-pects of traditional algorithms and deep learning algorithms.Finally,the opportunities and challenges faced by the point cloud qual-ity evaluation algorithm are summarized and prospected.This paper can provide guidance and reference for the optimization of point cloud data and the improvement of the compression algorithm.

point cloudquality evaluationpoint cloud compressiondeep learning

陈婧、魏宏安

展开 >

福州大学物理与信息工程学院,福州 350108

点云 质量评价 点云压缩 深度学习

2024

现代计算机
中大控股

现代计算机

影响因子:0.292
ISSN:1007-1423
年,卷(期):2024.30(23)