Triplet Relationship Guided Sampling Consensus for Robust Model Estimation
Hanlin Guo, Yang Lu, Guobao Xiao, Shuyuan Lin, Hanzi Wang
Abstract
RANSAC (RANdom SAmple Consensus) is a widely used robust estimator for estimating a geometric model from feature matches in an image pair. Unfortunately, it becomes less effective when initial input feature matches (i.e., input data) are corrupted by a large number of outliers. In this paper, we propose a new robust estimator (called TRESAC) for model estimation, where data subsets are sampled with the guidance of the triplet relationships, which involve high relevance and local geometric consistency. Each triplet consists of three data, whose relationships satisfy spatial consistency constraints. Therefore, the triplet relationships can be used to effectively initialize and refine the sampling process. With the advantage of the triplet relationships, TRESAC significantly alleviates the influence of outliers and also improves the computational efficiency of model estimation. Experimental results on four challenging datasets show that TRESAC can achieve superior performance on both estimation accuracy and computational efficiency against several other state-of-the-art methods.