Title
Dense Image Registration And Deformable Surface Reconstruction In Presence Of Occlusions And Minimal Texture
Abstract
Deformable surface tracking from monocular images is well-known to be under-constrained. Occlusions often make the task even more challenging, and can result in failure if the surface is not sufficiently textured. In this work, we explicitly address the problem of 3D reconstruction of poorly textured, occluded surfaces, proposing a framework based on a template-matching approach that scales dense robust features by a relevancy score. Our approach is extensively compared to current methods employing both local feature matching and dense template alignment. We test on standard datasets as well as on a new dataset (that will be made publicly available) of a sparsely textured, occluded surface. Our framework achieves state-of-the-art results for both well and poorly textured, occluded surfaces.
Year
DOI
Venue
2015
10.1109/ICCV.2015.262
2015 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV)
Field
DocType
Volume
Computer vision,Surface reconstruction,Pattern recognition,Computer science,Surface tracking,Feature matching,Artificial intelligence,Monocular,Image registration,3D reconstruction
Conference
2015
Issue
ISSN
Citations 
1
1550-5499
5
PageRank 
References 
Authors
0.40
33
6
Name
Order
Citations
PageRank
Dat Tien Ngo1312.13
Sanghyuk Park2101.72
Anne Jorstad3464.74
Alberto Crivellaro4442.14
Chang D. Yoo537545.88
Pascal Fua612768731.45