Surface Reconstruction

Surface reconstruction aims to create accurate 3D models from various input data, such as images, point clouds, or sensor readings, with a primary objective of achieving high-fidelity geometric detail and efficient processing. Recent research heavily emphasizes implicit neural representations, particularly Gaussian splatting and signed distance functions (SDFs), often combined with techniques like planar-based representations and multi-view consistency constraints to improve accuracy and scalability. These advancements are crucial for applications ranging from autonomous driving and urban planning to scientific visualization and cultural heritage preservation, enabling more realistic simulations and detailed analyses of complex 3D scenes.