MambaGaze-Stereo: Enhanced Depth Estimation for Autonomous Driving via Selective Attention and State Space Models

In autonomous driving, accurate and real-time depth estimation is essential for precise environmental perception and safe decision-making. However, traditional stereo matching approaches often struggle with capturing long-range dependencies, preserving fine details, and achieving robust disparity estimation in complex scenes. To address these challenges, we introduce MambaGaze-Stereo, a stereo matching framework based on State Space Models (SSM). Our framework includes a MobileNetV4-based feature extraction module, which significantly enhances multi-scale feature representation. We further incorporate a FlashAttention-based Feature Correlation Enhancement Module (FACEM), which utilizes an efficient attention mechanism to improve feature correlation and disparity estimation accuracy. Additionally, we propose the Mamba Selective Multi-Update Block (MSMUB), leveraging bidirectional selective attention to enable adaptive feature modeling across various scales, meeting the spatial information requirements of complex driving scenarios. Evaluated on the KITTI2012 dataset, MambaGaze-Stereo achieves an Out-Noc error rate of 0.84% in non-occluded regions and an Out-All error rate of 1.09% across all regions, outperforming existing state-of-the-art models and securing the top position. Our method ofers a compelling solution for high-precision stereo matching tasks in autonomous driving applications.