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On evaluating consensus in RANSAC surface registration
Author(s) -
Hruda L.,
Dvořák J.,
Váša L.
Publication year - 2019
Publication title -
computer graphics forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 120
eISSN - 1467-8659
pISSN - 0167-7055
DOI - 10.1111/cgf.13798
Subject(s) - ransac , rigid transformation , computer science , transformation (genetics) , metric (unit) , set (abstract data type) , similarity (geometry) , construct (python library) , transformation matrix , artificial intelligence , data mining , image registration , algorithm , simple (philosophy) , theoretical computer science , image (mathematics) , biochemistry , chemistry , operations management , physics , kinematics , classical mechanics , economics , gene , programming language , philosophy , epistemology
Random Sample Consensus is a powerful paradigm that was successfully applied in various contexts, including Location Determination Problem, fundamental matrix estimation and global 3D surface registration, where many previously proposed algorithms can be interpreted as a particular implementation of this concept. In general, a set of candidate transformations is generated by some simple procedure, and an aligning transformation is chosen within this set, such that it aligns the largest portion of the input data. We observe that choosing the aligning transformation may also be interpreted as finding consensus among the candidates, which in turn involves measuring similarity of candidate rigid transformations. While it is not difficult to construct a metric that provides reasonable results, most approaches come with certain limitations and drawbacks. In this paper, we investigate possible means of measuring distances in SE(3) and compare their properties both theoretically and experimentally in a model RANSAC registration algorithm. We also propose modifications to existing measures and propose a novel method of locating the consensus transformation based on Vantage Point Tree data structure.