@inproceedings{504fe1bed4d64b1fb331149e77ad510f,
title = "Contour-enhanced resampling of 3D point clouds via graphs",
abstract = "To reduce storage and computational cost for processing and visualizing large-scale 3D point clouds, an efficient resampling strategy is needed to select a representative subset of 3D points that can preserve contours in the original 3D point cloud. We tackle this problem by using graph-based techniques as graphs can represent underlying surfaces and lend themselves well to efficient computation. We first construct a general graph for a 3D point cloud and then propose a graph-based metric to quantify the contour information via high-pass graph filtering. Finally, we obtain an optimal resampling distribution that preserves the contour information by solving an optimization problem. When browsing, the proposed graph-based resampling performs better than uniform resampling both for toy point clouds as well as real large-scale point clouds. Furthermore, as neither mesh construction nor surface normal calculation is involved, the proposed graph-based method is computationally more efficient than the mesh-based methods.",
keywords = "3D point cloud, graph signal processing, high-pass filtering, resampling strategy",
author = "Siheng Chen and Dong Tian and Chen Feng and Anthony Vetro and Jelena Kovacevic",
year = "2017",
month = jun,
day = "16",
doi = "10.1109/ICASSP.2017.7952695",
language = "English (US)",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "2941--2945",
booktitle = "2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings",
note = "2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 ; Conference date: 05-03-2017 Through 09-03-2017",
}