Paper Info
Title
Fast and Globally Optimal Rigid Registration of 3D Point Sets by Transformation Decomposition.
Abstract
The rigid registration of two 3D point sets is a fundamental problem in computer vision. The current trend is to solve this problem globally using the BnB optimization framework. However, the existing global methods are slow for two main reasons: the computational complexity of BnB is exponential to the problem dimensionality (which is six for 3D rigid registration), and the bound evaluation used in BnB is inefficient. In this paper, we propose two techniques to address these problems. First, we introduce the idea of translation invariant vectors, which allows us to decompose the search of a 6D rigid transformation into a search of 3D rotation followed by a search of 3D translation, each of which is solved by a separate BnB algorithm. This transformation decomposition reduces the problem dimensionality of BnB algorithms and substantially improves its efficiency. Then, we propose a new data structure, named 3D Integral Volume, to accelerate the bound evaluation in both BnB algorithms. By combining these two techniques, we implement an efficient algorithm for rigid registration of 3D point sets. Extensive experiments on both synthetic and real data show that the proposed algorithm is three orders of magnitude faster than the existing state-of-the-art global methods.
Year
Venue
DocType
2018
arXiv: Computer Vision and Pattern Recognition
Journal
Volume
Citations 
PageRank 
abs/1812.11307
0
0.34
References 
Authors
18
6
Name
Order
Citations
PageRank
Xuechen Li122.39
Yinlong Liu22613.51
Yiru Wang322.06
Chen Wang436193.70
Manning Wang574.27
Zhijian Song63112.34