Neural SDFs

Neural signed distance functions (Neural SDFs) represent 3D shapes implicitly as a function that outputs the distance to the surface from any point in space. Current research focuses on improving the accuracy, efficiency, and generalizability of these representations, employing techniques like graph transformers, view-dependent normal compensation, and truncated signed distance fields (TSDFs) to enhance reconstruction quality and inference speed. These advancements are impacting various fields, including computer graphics (e.g., real-time view synthesis, meshing), computer vision (e.g., 3D reconstruction from sparse point clouds or images), and materials science (e.g., accelerating electronic structure calculations). The ultimate goal is to create robust and efficient methods for representing and manipulating complex 3D shapes across diverse applications.