TY - GEN
T1 - Nonrigid surface registration and completion from RGBD images
AU - Xu, Weipeng
AU - Salzmann, Mathieu
AU - Wang, Yongtian
AU - Liu, Yue
PY - 2014
Y1 - 2014
N2 - Nonrigid surface registration is a challenging problem that suffers from many ambiguities. Existing methods typically assume the availability of full volumetric data, or require a global model of the surface of interest. In this paper, we introduce an approach to nonrigid registration that performs on relatively low-quality RGBD images and does not assume prior knowledge of the global surface shape. To this end, we model the surface as a collection of patches, and infer the patch deformations by performing inference in a graphical model. Our representation lets us fill in the holes in the input depth maps, thus essentially achieving surface completion. Our experimental evaluation demonstrates the effectiveness of our approach on several sequences, as well as its robustness to missing data and occlusions.
AB - Nonrigid surface registration is a challenging problem that suffers from many ambiguities. Existing methods typically assume the availability of full volumetric data, or require a global model of the surface of interest. In this paper, we introduce an approach to nonrigid registration that performs on relatively low-quality RGBD images and does not assume prior knowledge of the global surface shape. To this end, we model the surface as a collection of patches, and infer the patch deformations by performing inference in a graphical model. Our representation lets us fill in the holes in the input depth maps, thus essentially achieving surface completion. Our experimental evaluation demonstrates the effectiveness of our approach on several sequences, as well as its robustness to missing data and occlusions.
KW - Nonrigid registration
KW - RGBD images
KW - surface completion
UR - http://www.scopus.com/inward/record.url?scp=84906510463&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-10605-2_5
DO - 10.1007/978-3-319-10605-2_5
M3 - Conference contribution
AN - SCOPUS:84906510463
SN - 9783319106045
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 64
EP - 79
BT - Computer Vision, ECCV 2014 - 13th European Conference, Proceedings
PB - Springer Verlag
T2 - 13th European Conference on Computer Vision, ECCV 2014
Y2 - 6 September 2014 through 12 September 2014
ER -