AI RESEARCH
Global Truncated Loss Minimization for Robust and Threshold-Resilient Geometric Estimation
arXiv CS.CV
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ArXi:2603.14796v1 Announce Type: new To achieve outlier-robust geometric estimation, robust objective functions are generally employed to mitigate the influence of outliers. The widely used consensus maximization(CM) is highly robust when paired with global branch-and-bound(BnB) search. However, CM relies solely on inlier counts and is sensitive to the inlier threshold. Besides, the discrete nature of CM leads to loose bounds, necessitating extensive BnB iterations and computation cost.