· 2012
Real-time compression of point cloud streams
- 338citations
- 2012year
Short summary
A novel lossy compression method achieves compression ratios of 14:1 (at 1mm precision) and 40:1 (at 9mm precision) for point cloud streams by exploiting spatial and temporal redundancy.
AI-generated from the title and abstract; the full text is not read.
Key points
- Novel lossy compression approach for point cloud streams.
- Exploits spatial redundancy using octree data structures.
- Exploits temporal redundancy by encoding differences between consecutive octrees.
- Achieves compression ratios of 14:1 (1mm precision) and 40:1 (9mm precision).
AI-generated from the title and abstract; the full text is not read.
Abstract
We present a novel lossy compression approach for point cloud streams which exploits spatial and temporal redundancy within the point data. Our proposed compression framework can handle general point cloud streams of arbitrary and varying size, point order and point density. Furthermore, it allows for controlling coding complexity and coding precision. To compress the point clouds, we perform a spatial decomposition based on octree data structures. Additionally, we present a technique for comparing the octree data structures of consecutive point clouds. By encoding their structural differences, we can successively extend the point clouds at the decoder. In this way, we are able to detect and remove temporal redundancy from the point cloud data stream. Our experimental results show a strong compression performance of a ratio of 14 at 1 mm coordinate precision and up to 40 at a coordinate precision of 9 mm.
The authors' abstract, as published at the source., 2012 · DOI ↗
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Field: Computer Graphics and Computer-Aided Design
Computer Graphics and Computer-Aided DesignComputer Science