Monocular Vision

Monocular vision research focuses on extracting three-dimensional information and understanding from single-camera images, aiming to overcome the inherent limitations of depth ambiguity. Current efforts concentrate on developing robust and efficient algorithms, often employing convolutional neural networks, transformers, and techniques like photometric SLAM and 3D Gaussian splatting, to achieve tasks such as depth estimation, pose estimation, and scene reconstruction. These advancements have significant implications for various fields, including autonomous driving, robotics, augmented reality, and medical applications, by enabling more sophisticated and cost-effective visual perception systems.