LiDAR-Guided Stereo Matching Using Bayesian Optimization With Gaussian Process Regression

Abstract

LiDAR-guided stereo matching for high-precision disparity estimation is a very promising task in photogrammetry and remote sensing. Unfortunately, existing methods suffer from the problem that it is difficult to automatically obtain appropriate stereo matching model parameters to ensure satisfactory results. To solve it, this letter proposes a LiDAR-guided stereo matching framework using Bayesian optimization with Gaussian process regression, which aims to automatically infer the stereo matching model parameters by LiDAR data. First, local matching model based on the belief propagation algorithm is designed. Second, the objective function is constructed by minimizing the difference between the local matching results and the LiDAR data. Third, Bayesian optimization with Gaussian process regression is applied to minimize this objective function to infer the model parameters. Finally, experimental results on the GaoFen-7 and UAV Stereo datasets show that the proposed method can effectively infer suitable model parameters from LiDAR data, and our method outperforms the state-of-the-art methods.