Please use this identifier to cite or link to this item: https://doi.org/10.1006/cviu.2002.0987
Title: Compression of dynamic 3D geometry data using iterative closest point algorithm
Authors: Gupta, S.
Sengupta, K. 
Kassim, A.A. 
Issue Date: Jul-2002
Source: Gupta, S., Sengupta, K., Kassim, A.A. (2002-07). Compression of dynamic 3D geometry data using iterative closest point algorithm. Computer Vision and Image Understanding 87 (1-3) : 116-130. ScholarBank@NUS Repository. https://doi.org/10.1006/cviu.2002.0987
Abstract: In this paper, we propose a new framework to perform motion compression for time-dependent 3D geometric data. Temporal coherence in dynamic geometric models can be used to achieve significant compression, thereby leading to efficient storage and transmission of large volumes of 3D data. The displacement of the vertices in the geometric models is computed using the iterative closest point (ICP) algorithm. This forms the core of our motion technique and is used to estimate the transformation between two successive 3D data sets. The motion between frames is coded in terms of a few affine parameters with some added residues. Our motion segmentation approach separates the vertices into two groups. Within the first group, motion can be encoded with a few affine parameters without the need of residues. In the second group, the vertices need further encoding of residual errors. Also in this group, for those vertices associated with large residual errors under affine mapping, we encode their motion effectively using Newtonian motion estimates. This automatic segmentation enables our algorithm to be very effective in compressing time-dependent geometric data. Dynamic range data captured from the real world, as well as complex animations created using commercial tools, can be compressed efficiently using this scheme.
Source Title: Computer Vision and Image Understanding
URI: http://scholarbank.nus.edu.sg/handle/10635/55377
ISSN: 10773142
DOI: 10.1006/cviu.2002.0987
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