Implicit Surface Representation

Implicit surface representation uses neural networks to encode 3D shapes as continuous functions, primarily signed distance functions (SDFs), offering memory efficiency and the ability to represent complex geometries. Current research focuses on improving reconstruction accuracy, particularly for challenging scenarios like sparse views, strong lighting, and semi-transparent objects, often employing techniques like octrees, Gaussian splatting, and hybrid models combining implicit and explicit representations. These advancements are significantly impacting 3D reconstruction in fields such as robotics, computer vision, and digital content creation, enabling more efficient and accurate modeling of complex scenes and objects.