Signed Distance Field

Signed distance fields (SDFs) are implicit surface representations that define a 3D shape by assigning a signed distance value to each point in space, with positive values outside, negative values inside, and zero on the surface. Current research focuses on improving SDF reconstruction accuracy and efficiency using various neural network architectures, including multi-layer perceptrons (MLPs) and Gaussian splatting, often incorporating techniques like centroidal Voronoi tessellation for optimized sampling and truncated SDFs for faster computation. These advancements are driving progress in diverse applications such as 3D modeling, inverse rendering, robotic navigation, and medical image analysis, enabling more accurate and efficient processing of complex 3D data. The development of robust and efficient SDF methods is crucial for advancing fields reliant on accurate and detailed 3D scene understanding.