3D Geometric Prior

3D geometric priors are foundational elements in computer vision and graphics, used to improve the accuracy and efficiency of tasks like 3D scene reconstruction, image synthesis, and multi-view compression. Current research focuses on integrating these priors into various model architectures, including diffusion models and neural radiance fields (NeRFs), often leveraging techniques like Gaussian splatting and cost volume construction to represent and manipulate 3D shape and structure. This work aims to address limitations in existing methods, such as the need for multiple input views or the generation of inconsistent 3D geometries, leading to more robust and controllable 3D content generation and improved compression performance. The resulting advancements have significant implications for applications ranging from autonomous driving to virtual and augmented reality.