Monocular Prediction

Monocular prediction focuses on inferring 3D scene properties, such as depth, object pose, and motion, from a single image, aiming to overcome the limitations of traditional stereo vision. Current research emphasizes leveraging deep learning architectures, including convolutional neural networks (CNNs) and transformers, often incorporating geometric constraints and physical priors to improve accuracy and robustness, particularly in challenging scenarios like crowded scenes or dynamic environments. These advancements have implications for various applications, including autonomous driving (predicting maneuvers), human-computer interaction (3D human reconstruction), and robotics (scene understanding), by enabling more efficient and reliable perception systems.