Disparity Map

Disparity maps represent the distance between corresponding points in a stereo image pair, providing crucial depth information for 3D scene reconstruction. Current research focuses on improving the accuracy and efficiency of disparity map estimation, particularly addressing challenges like handling occlusions, large disparities, and temporal inconsistencies in video. This involves developing advanced deep learning architectures, such as recurrent neural networks and transformer-based models, often incorporating iterative refinement and multi-modal fusion techniques with other sensor data (e.g., LiDAR). Accurate disparity maps are fundamental to numerous applications, including autonomous driving, robotics, 3D modeling, and remote sensing.