Disparity Plane Fitting Based on Random Sampling

In this article, we discuss the methodology of disparity plane fitting based on random sampling and elaborate on its implementation steps. This approach proves valuable for extracting depth information from images, playing a significant role in applications such as 3D reconstruction and scene understanding. Specifically, the method employs random sampling to select point pairs within images and estimates disparity planes by fitting these sampled pairs. The implementation typically involves using RANSAC (Random Sample Consensus) algorithm which randomly selects minimal point sets to compute plane hypotheses and iteratively refines the model based on inlier counts. Due to the stochastic nature of sampling, the method exhibits strong robustness, effectively handling noise and outliers in images. The core algorithm workflow includes: 1) random sampling of pixel correspondences, 2) plane parameter calculation using linear regression or least-squares fitting, 3) inlier/outlier classification based on disparity error thresholds, and 4) iterative optimization until convergence. Key functions would involve implementing sampling routines, plane equation solvers, and error evaluation metrics. Furthermore, through analysis of the estimated disparity planes, more accurate depth information can be obtained, thereby enhancing the performance of subsequent applications. Thus, random sampling-based disparity plane fitting represents a highly valuable technique worthy of further research and application in computer vision systems.