Globfit: Consistently fitting primitives by discovering global relations
Type
Conference PaperKAUST Department
Computer Science ProgramComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Visual Computing Center (VCC)
Date
2011-07-26Online Publication Date
2011-07-26Print Publication Date
2011Permanent link to this record
http://hdl.handle.net/10754/575901
Metadata
Show full item recordAbstract
Given a noisy and incomplete point set, we introduce a method that simultaneously recovers a set of locally fitted primitives along with their global mutual relations. We operate under the assumption that the data corresponds to a man-made engineering object consisting of basic primitives, possibly repeated and globally aligned under common relations. We introduce an algorithm to directly couple the local and global aspects of the problem. The local fit of the model is determined by how well the inferred model agrees to the observed data, while the global relations are iteratively learned and enforced through a constrained optimization. Starting with a set of initial RANSAC based locally fitted primitives, relations across the primitives such as orientation, placement, and equality are progressively learned and conformed to. In each stage, a set of feasible relations are extracted among the candidate relations, and then aligned to, while best fitting to the input data. The global coupling corrects the primitives obtained in the local RANSAC stage, and brings them to precise global alignment. We test the robustness of our algorithm on a range of synthesized and scanned data, with varying amounts of noise, outliers, and non-uniform sampling, and validate the results against ground truth, where available. © 2011 ACM.Citation
Li, Y., Wu, X., Chrysathou, Y., Sharf, A., Cohen-Or, D., & Mitra, N. J. (2011). GlobFit. ACM SIGGRAPH 2011 Papers on - SIGGRAPH ’11. doi:10.1145/1964921.1964947Conference/Event name
ACM SIGGRAPH 2011, SIGGRAPH 2011ISBN
9781450309431ae974a485f413a2113503eed53cd6c53
10.1145/1964921.1964947