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The ability to generate good model hypotheses is instrumental to accurate and robust geometric model fitting. We present a novel dynamic hypothesis generation algorithm for robust fitting of multiple structures. Underpinning our method is a fast guided sampling scheme enabled by analysing correlation of preferences induced by data and hypothesis residuals. Our method progressively accumulates evidence in the search space, and uses the information to dynamically (1) identify outliers, (2) filter unpromising hypotheses, and (3) bias the sampling for active discovery of multiple structures in the data-All achieved without sacrificing the speed associated with sampling-based methods. Our algorithm yields a disproportionately higher number of good hypotheses among the sampling outcomes, i.e., most hypotheses correspond to the genuine structures in the data. This directly supports a novel hierarchical model fitting algorithm that elicits the underlying stratified manner in which the structures are organized, allowing more meaningful results than traditional “flat” multi-structure fitting.
Date of Conference: 6-13 Nov. 2011