SIDDEQ, M.M. and RODRIGUES, Marcos (2016). 3D Point Cloud Data and Triangle Face Compression by a Novel Geometry Minimization Algorithm and Comparison with other 3D Formats. Proceedings of the international conference on computational methods, 3, 379-394.
Rodrigues 3D Point Cloud Data.pdf - Accepted Version
Available under License Creative Commons Public Domain Dedication.
Download (2MB) | Preview
PDF (Editorial decision of acceptance)
[ICCM2016] Editorial Decision on Paper.pdf
Restricted to Repository staff only
Polygonal meshes remain the primary representation for visualization of 3D data in a wide range of industries including manufacturing, architecture, geographic information systems, medical imaging, robotics, entertainment, and military applications. Because of its widespread use, it is desirable to compress polygonal meshes stored in ﬁle servers and exchanged over computer networks to reduce storage and transmission time requirements. 3D files encoded by OBJ format are commonly used to share models due to its clear simple design. Normally each OBJ file contains a large amount of data (e.g. vertices and triangulated faces) describing the mesh surface. In this research we introduce a novel algorithm to compress vertices and triangle faces called Geometry Minimization Algorithm (GM-Algorithm). First, each vertex consists of (x, y, z) coordinates that are encoded into a single value by the GM-Algorithm. Second, triangle faces are encoded by computing the differences between two adjacent vertex locations, and then coded by the GM-Algorithm followed by arithmetic coding. We tested the method on large data sets achieving high compression ratios over 90% while keeping the same number of vertices and triangle faces as the original mesh. The decompression step is based on a Parallel Fast Matching Search Algorithm (Parallel-FMS) to recover the structure of the 3D mesh. A comparative analysis of compression ratios is provided with a number of commonly used 3D file formats such as MATLAB, VRML, OpenCTM and STL showing the advantages and effectiveness of our approach.
|Research Institute, Centre or Group:||Cultural Communication and Computing Research Institute > Communication and Computing Research Centre|
|Depositing User:||Marcos Rodrigues|
|Date Deposited:||31 Mar 2016 13:15|
|Last Modified:||25 Feb 2017 20:47|
Actions (login required)
Downloads per month over past year