Monocular RGB

Monocular RGB research focuses on extracting 3D information, such as object pose, shape, and scene structure, from a single color image, aiming to overcome the limitations of relying on multiple views or depth sensors. Current efforts concentrate on developing robust and efficient deep learning models, often employing neural implicit representations, convolutional neural networks, and transformers, to address challenges like occlusion, symmetry, and varying object categories. These advancements have significant implications for applications such as augmented reality, robotics, and autonomous navigation, enabling more accurate and efficient 3D scene understanding from readily available RGB data.