Disparity Estimation

Disparity estimation, the process of determining the distance between corresponding points in multiple images, is crucial for 3D scene reconstruction and depth perception in computer vision. Current research focuses on improving accuracy and efficiency through novel deep learning architectures, including transformer-based models and recurrent networks, often incorporating techniques like cost volume aggregation, multi-scale processing, and uncertainty estimation to handle challenging scenarios such as occlusions and low-texture regions. These advancements are driving progress in applications ranging from autonomous driving and robotics to medical imaging and satellite imagery analysis, where accurate depth information is essential. Furthermore, research is exploring the use of diverse sensor modalities, such as event cameras and quad-pixel sensors, to enhance disparity estimation capabilities.