Unsigned Distance Field

Unsigned distance fields (UDFs) are a versatile representation for 3D shapes, particularly those with complex topologies or open surfaces, unlike signed distance fields which are limited to watertight objects. Current research focuses on improving the accuracy and efficiency of extracting surfaces from UDFs, often employing neural networks and novel algorithms like marching cubes adaptations or diffusion models to address challenges such as non-differentiability at the zero level set and topological inconsistencies. These advancements are significantly impacting 3D reconstruction, shape generation, and compression, enabling more robust and detailed handling of diverse 3D models in various applications.