Monocular 3D

Monocular 3D reconstruction aims to create three-dimensional models of scenes or objects from a single camera view, a challenging inverse problem due to inherent ambiguities. Current research focuses on improving accuracy and efficiency through various approaches, including neural radiance fields (NeRFs), transformers, diffusion models, and Gaussian splatting, often incorporating self-supervision and leveraging priors like ground planes or human body models to constrain the solution space. These advancements are significant for applications such as robotics, augmented reality, autonomous driving, and human motion capture, enabling more robust and cost-effective 3D perception systems.