Name
Abstract | ||
|---|---|---|
In this paper, we propose a sphere-projection-based framework for point cloud geometry and attribute lossless and lossy coding. The original point cloud is adaptively divided into blocks, and then we create a fitting sphere in each block for modeling the local geometry structure of the point cloud. Sphere coordination transform and spherical projection scheme are introduced to transfer a 3D point cloud to a set of the range images. A novel compact representation of generated range images based on Morton codes is proposed to separate the range images into occupancy images and attributes vectors for further compression. Experimental results demonstrate that for the LiDAR point clouds datasets in lossless compression, the proposed method offers better performance than geometry-based point cloud compression (G-PCC). For the object point clouds datasets in lossy compression, the proposed method has better rate-distortion (R-D) performance than Draco. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/VCIP49819.2020.9301809 | 2020 IEEE International Conference on Visual Communications and Image Processing (VCIP) |
Keywords | DocType | ISSN |
point cloud,lossless compression,lossy compression,spherical projection,range images | Conference | 1018-8770 |
ISBN | Citations | PageRank |
978-1-7281-8069-4 | 0 | 0.34 |
References | Authors | |
3 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yingshen He | 1 | 0 | 0.34 |
| Ge Li | 2 | 0 | 0.68 |
| Yiting Shao | 3 | 15 | 3.61 |
| Jing Wang | 4 | 1 | 1.74 |
| Yueru Chen | 5 | 0 | 0.34 |
| Shan Liu | 6 | 0 | 0.34 |