Paper Info
Title
Non-rigid Registration Meets Surface Reconstruction
Abstract
Non rigid registration is an important task in computer vision with many applications in shape and motion modeling. A fundamental step of the registration is the data association between the source and the target sets. Such association proves difficult in practice, due to the discrete nature of the information and its corruption by various types of noise, e.g. Outliers and missing data. In this paper we investigate the benefit of the implicit representations for the non-rigid registration of 3D point clouds. First, the target points are described with small quadratic patches that are blended through partition of unity weighting. Then, the discrete association between the source and the target can be replaced by a continuous distance field induced by the interface. By combining this distance field with a proper deformation term, the registration energy can be expressed in a linear least square form that is easy and fast to solve. This significantly eases the registration by avoiding direct association between points. Moreover, a hierarchical approach can be easily implemented by employing coarse-to-fine representations. Experimental results are provided for point clouds from multi-view data sets. The qualitative and quantitative comparisons show the out performance and robustness of our framework.
Year
DOI
Venue
2014
10.1109/3DV.2014.80
2014 2nd International Conference on 3D Vision
Keywords
Field
DocType
Surface Registration,Correspondence-Free,Non-Rigid Deformation,Surface Reconstruction,Implicit Representation,Partition of Unity
Computer vision,Partition of unity,Point set registration,Weighting,Outlier,Robustness (computer science),Distance transform,Artificial intelligence,Missing data,Point cloud,Mathematics
Conference
Volume
ISSN
Citations 
1
1550-6185
4
PageRank 
References 
Authors
0.39
35
3
Name
Order
Citations
PageRank
Mohammad Rouhani1515.30
Edmond Boyer22758130.84
Angel Domingo Sappa340.39