TY - JOUR
T1 - Compression of textured surfaces represented as surfel sets
AU - Darom, T.
AU - Ruggeri, M. R.
AU - Saupe, D.
AU - Kiryati, N.
N1 - Funding Information:
This research was supported by the Kurt Lion Foundation. At Tel-Aviv University, it was supported by the Ministry of Science. At Konstanz University, it was supported by the DFG Graduiertenkolleg “Explorative Analysis and Visualization of Large Information Spaces”.
PY - 2006/10
Y1 - 2006/10
N2 - A method for lossy compression of genus-0 surfaces is presented. Geometry, texture and other surface attributes are incorporated in a unified manner. The input surfaces are represented by surfels (surface elements), i.e., by a set of disks with attributes. Each surfel, with its attribute vector, is optimally mapped onto a sphere in the sense of geodesic distance preservation. The resulting spherical vector-valued function is resampled. Its components are decorrelated by the Karhunen-Loève transform, represented by spherical wavelets and encoded using the zerotree algorithm. Methods for geodesic distance computation on surfel-based surfaces are considered. A novel efficient approach to dense surface flattening/mapping, using rectangular distance matrices, is employed. The distance between each surfel and a set of key-surfels is optimally preserved, leading to greatly improved resolution and eliminating the need for interpolation, that complicates and slows down existing surface unfolding methods. Experimental surfel-based surface compression results demonstrate successful compression at very low bit rates.
AB - A method for lossy compression of genus-0 surfaces is presented. Geometry, texture and other surface attributes are incorporated in a unified manner. The input surfaces are represented by surfels (surface elements), i.e., by a set of disks with attributes. Each surfel, with its attribute vector, is optimally mapped onto a sphere in the sense of geodesic distance preservation. The resulting spherical vector-valued function is resampled. Its components are decorrelated by the Karhunen-Loève transform, represented by spherical wavelets and encoded using the zerotree algorithm. Methods for geodesic distance computation on surfel-based surfaces are considered. A novel efficient approach to dense surface flattening/mapping, using rectangular distance matrices, is employed. The distance between each surfel and a set of key-surfels is optimally preserved, leading to greatly improved resolution and eliminating the need for interpolation, that complicates and slows down existing surface unfolding methods. Experimental surfel-based surface compression results demonstrate successful compression at very low bit rates.
KW - Geodesic paths
KW - Spherical mapping
KW - Spherical wavelets
KW - Surfels
KW - Textured surface compression
UR - http://www.scopus.com/inward/record.url?scp=33750083354&partnerID=8YFLogxK
U2 - 10.1016/j.image.2006.07.003
DO - 10.1016/j.image.2006.07.003
M3 - מאמר
AN - SCOPUS:33750083354
VL - 21
SP - 770
EP - 786
JO - Signal Processing: Image Communication
JF - Signal Processing: Image Communication
SN - 0923-5965
IS - 9
ER -