A Framework for Compressive-Sensing of 3D Point Clouds
| Autor: | Patrick Chiang, Vahid Behravan, Gurjeet Singh |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Computer science
business.industry 020208 electrical & electronic engineering Point cloud 02 engineering and technology Iterative reconstruction Compressed sensing Lidar Compression (functional analysis) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Computer vision Enhanced Data Rates for GSM Evolution Artificial intelligence business Algorithm |
| Zdroj: | CIS |
| DOI: | 10.1109/cis.2016.0024 |
| Popis: | In this paper we propose a framework for analyzing 3D point cloud data compression using compressive-sensing. This framework uses real 3D point clouds and investigates different error sources that may affect performance of the system. Experimental results show that excluding edge points from error calculation gives us better criteria to decide the best compression ratio in the system. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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