点云
计算机科学
计算
云计算
图形
最优化问题
滤波器(信号处理)
不变(物理)
算法
分布式计算
理论计算机科学
数学优化
人工智能
计算机视觉
数学
操作系统
数学物理
作者
Xuhong Zhou,Yan Zeng,Zhou Wu,Jiepeng Liu
出处
期刊:IEEE Transactions on Consumer Electronics
[Institute of Electrical and Electronics Engineers]
日期:2023-08-01
卷期号:69 (3): 608-621
标识
DOI:10.1109/tce.2023.3272773
摘要
In many consumer electronic applications, three-dimensional (3D) point cloud data (PCD) is commonly generated in data-driven models. The expensive burden of computation and storage would be brought out, as the point number reaches billions in the large-scale augmented or virtual reality models. Based on the concept of graph signal, a new PCD simplification approach is developed to handle the high-volume PCD and develop a feature-controllable mechanism. In particular, PCD simplification is modeled as an optimization problem with the hybridization of contour and planar feature items. To reduce the complexity of computation, invariant decomposition is proposed, and distributed optimization is developed for the feature controllable PCD simplification. The self-adoption scheme is adopted to improve the convergence rate. Verification experiments and applications on different scenarios demonstrate the effectiveness and feasibility of the proposed simplification approach.
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