Implicit Surface

Implicit surfaces represent 3D shapes as the zero-level set of a continuous function, offering advantages in representing complex geometries and handling topology changes. Current research focuses on improving the accuracy and efficiency of these representations, particularly using neural networks (e.g., neural radiance fields, signed distance functions) to learn implicit surfaces from various data sources like point clouds and multi-view images. This active area of research is driving advancements in 3D reconstruction, shape modeling, and rendering, with applications spanning computer graphics, robotics, and medical imaging.