Name
Abstract | ||
|---|---|---|
Continuous point cloud data has become very important recently as a key component in the development of autonomous driving technology, and has in fact become indispensable for some autonomous driving applications such as obstacle detection. However, such large amounts of data are very expensive to store and are difficult to share directly due to their volume. Previous studies have explored various methods of compressing point cloud data directly by converting it into 2D images or by using tree-based approaches. In this study, rather than compress point cloud data directly, we choose to compress packet data which is raw point cloud data, by converting it losslessly into range images, and then using various image/video compression algorithms to reduce the volume of the data. In this paper, four methods, which are based on MPEG, JPEG and two kinds of preprocessing approaches for each method, are evaluated for range images compression. PSNR V.S Bitrate and RMSE V.S Bitrate are used to compare and evaluate the performance of these four methods. As whether a lossy compression methods is good always depending on the application, a localization application to test point cloud data reconstruction performance is also conducted. By comparing these methods, some important conclusions can be got. |
Year | Venue | Field |
|---|---|---|
2016 | 2016 IEEE 19TH INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC) | Iterative reconstruction,Computer vision,Lossy compression,Computer science,Network packet,Transform coding,JPEG,Artificial intelligence,Point cloud,Data compression,Image compression |
DocType | Citations | PageRank |
Conference | 1 | 0.35 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chenxi Tu | 1 | 1 | 0.35 |
| Eijiro Takeuchi | 2 | 242 | 26.05 |
| Chiyomi Miyajima | 3 | 345 | 45.71 |
| Kazuya Takeda | 4 | 1301 | 195.60 |