Disparity Estimation Network

Disparity estimation networks aim to computationally determine depth information from images, crucial for applications like autonomous driving and 3D reconstruction. Current research focuses on improving accuracy and efficiency through various architectures, including recurrent networks leveraging temporal information (e.g., from event cameras or structured light systems), and multi-view approaches using trinocular or light-field data to overcome limitations of traditional stereo vision. These advancements are driving progress in robotics, medical imaging, and computer vision by enabling more robust and accurate 3D scene understanding from various sensor modalities.