Stereo Depth Estimation

Stereo depth estimation aims to reconstruct three-dimensional scene geometry from two or more images, mimicking human binocular vision. Current research emphasizes improving accuracy and efficiency using various approaches, including convolutional neural networks (CNNs), transformers, and event-based cameras, often incorporating self-supervised learning and innovative cost volume processing techniques to handle challenges like transparent objects and low-texture regions. These advancements are crucial for applications such as autonomous driving, robotics, and augmented reality, enabling more robust and reliable 3D perception in diverse environments. The field is also actively exploring methods to reduce computational complexity for resource-constrained devices.