3D Prior

3D priors are foundational representations of three-dimensional shapes and structures used to improve the accuracy and realism of various computer vision tasks. Current research focuses on integrating these priors, often learned from large datasets or derived from models like 3D Morphable Models (3DMMs) and Gaussian Splatting, into diverse algorithms such as diffusion models, neural radiance fields (NeRFs), and generative adversarial networks (GANs). This allows for improved image and video generation, reconstruction from limited data (e.g., single images or sparse views), and enhanced understanding of spatial relationships in scenes, leading to advancements in applications ranging from medical imaging to robotics.