Unsigned Distance Function

Unsigned distance functions (UDFs) are a numerical representation of 3D shapes, particularly useful for open surfaces where traditional signed distance functions fail. Current research focuses on improving the accuracy and efficiency of learning UDFs from various data sources (e.g., multi-view images, point clouds) using neural networks, often incorporating novel differentiable renderers or self-supervised learning techniques to overcome challenges like sparse sampling and discontinuous surfaces. These advancements enhance 3D shape reconstruction, surface representation learning, and related applications in computer vision and graphics, leading to more robust and accurate 3D models from diverse input data.