Distance Field

Distance fields represent 3D shapes implicitly by encoding the distance from any point in space to the nearest surface point. Current research focuses on improving the accuracy and efficiency of these representations, particularly using neural networks to learn distance functions from various data sources (e.g., point clouds, images) and employing architectures like neural signed/unsigned distance fields (SDF/UDF) and directed distance fields (DDF). These advancements are driving progress in diverse applications, including 3D reconstruction, robotics (motion planning, manipulation), computer graphics (rendering), and medical imaging (prognosis prediction).