Guided Sampling by Neighborhood Information and Matching Scores for Multi-Structure Data
Taotao Lai, Jingyu Fan, Yizhang Liu, Rui Ming, Zuoyong Li · IEEE Signal Processing Letters · 2023
The success of most robust model estimation methods heavily relies on their used data sampling algorithms. This paper proposes a novel sampling algorithm, called Guided Sampling by Neighborhood Information and Matching Scores (NIMS), to efficiently sample promising hypotheses for fitting multi-structure data. Specifically, NIMS follows a specific sampling process. First, the proposed NIMS randomly selects a data point. Then, NIMS selects the neighbors of the selected data by using the neighborhood information to remove most of the outlier neighbors. Finally, NIMS samples a data subset using matching scores from the selected neighbors, which encourages NIMS to sample inliers from the selected neighbors. Experimental results on the publicly availableAdelaideRMFdataset demonstrate that the proposed NIMS outperforms several state-of-the-artsampling algorithms.