Broad feature extraction and multi-directional imbalanced weighted broad learning system for the unsupervised stereo matching method

The practical application of supervised stereo matching methods is often limited by the requirement of obtaining ground-truth disparity maps in advance. To address this issue, we introduce a novel unsupervised stereo matching method based on the broad feature extraction and multi-directional imbalanced weighted broad learning system. In this study, an end-to-end unsupervised stereo matching method based on broad feature extraction was developed to generate an initial disparity map. Subsequently, a multi-directional imbalanced weighted broad learning system that considers the uneven distribution of disparity values was designed to effectively remove and redefine outlier disparities and enhance the accuracy of generating multiple candidate disparity maps. Multi-direction consistency verification was integrated into the system to further eliminate anomalous disparity values. This verification relies on uniqueness, which guarantees the consistency of the disparity truth values at the same location in various directions. Finally, the local gravity weight method based on a broad feature space was introduced to select a suitable disparity value from neighboring pixels to replace invalid pixel positions. Experiments were conducted under both indoor and outdoor scenarios to demonstrate the effectiveness and versatility of our approach, confirming that it outperforms state-of-the-art techniques, highlighting its superiority.