@INPROCEEDINGS{8078873, 
author={L. Wang and L. Wang and Y. Luo and M. Liu}, 
booktitle={2017 IEEE International Conference on Information and Automation (ICIA)}, 
title={Point-Cloud compression using data independent method #x2014; A 3D discrete cosine transform approach}, 
year={2017}, 
volume={}, 
number={}, 
pages={1-6}, 
abstract={Point-cloud is a widely used representation for objects and scenes. It generally consists of a large amount of 3D coordinates of points describing reflective surfaces. A subtle problem is that the number of points is usually so large that real-time transmission and efficient storage is not feasible. In this work, we propose to use 3D Discrete Cosine Transform (3D-DCT) to compress these two typical categories of data, namely point-cloud data extracted from objects and environments (i.e. 3D maps). Experimental results show that the proposed method leads to high compression ratio and flexible reconstruction behaviors comparing with other related methods.}, 
keywords={data compression;discrete cosine transforms;feature extraction;image reconstruction;stereo image processing;3D discrete cosine transform;data independent method;high compression ratio;point-cloud compression;point-cloud data;reflective surfaces;Discrete cosine transforms;Error analysis;Image coding;Matrix converters;Quantization (signal);Real-time systems;Three-dimensional displays}, 
doi={10.1109/ICInfA.2017.8078873}, 
ISSN={}, 
month={July},}