SDF Diffusion

Signed Distance Functions (SDFs) are increasingly used to represent 3D shapes implicitly, enabling efficient and flexible 3D modeling and reconstruction. Current research focuses on improving the accuracy and efficiency of SDF-based methods, particularly for complex scenes and fine geometric details, often employing neural networks and diffusion models to learn and generate SDF representations from various inputs like images, point clouds, and even text descriptions. These advancements are impacting fields like robotics (collision avoidance, manipulation), computer vision (scene reconstruction, novel view synthesis), and computer graphics (shape generation, editing), offering improved accuracy and control in 3D modeling tasks. The development of novel algorithms, such as those incorporating attention mechanisms and efficient sampling strategies, continues to drive progress in this area.