Planar feature fitting based on Multi-BaySAC algorithm

Zhen Li; Zhizhong Kang

doi:10.1117/12.2031402

Abstract

To tackle the problem that classic RANSAC (Random Sample Consensus) is limited by the assumption that a single model accounts for all of the data inliers, an algorithm of multi-planar-feature fitting from 3D point cloud based on BaySAC algorithm (Bayes Sample Consensus) is proposed (called multiBaySAC). First, as the mathematical models of most of primitives to be fitted are determinate, a statistical algorithm of hypothesis model parameters histogram is proposed to detect potential planar features. Instead of assuming constant prior probabilities of data points and choosing initial data sets by random as RANSAC, we then implement a conditional sampling method -- BaySAC for robust parameters estimation of potential planar features, by computing the prior probability of each data point and updating the inlier probabilities using simplified Bayes’ rule. For the purpose of multiple feature fitting, the sequential application of the above procedure is implemented following the removal of the detected set of inliers. The proposed approach is tested with point cloud data of buildings acquired by RIEGL VZ-400 laser scanner. The results show that the proposed Multi-BaySAC can achieve high computation efficiency and fitting accuracy of multiple planar feature fitting.

Citation Download Citation

Zhen Li, Zhen Li, Zhizhong Kang, Zhizhong Kang, "Planar feature fitting based on Multi-BaySAC algorithm", Proc. SPIE 8919, MIPPR 2013: Pattern Recognition and Computer Vision, 89190U (27 October 2013); doi: 10.1117/12.2031402; https://doi.org/10.1117/12.2031402

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