Neural Surface Field

Neural surface fields represent a powerful approach to reconstructing 3D scenes and objects from images, aiming to recover detailed geometry, materials, and lighting conditions. Current research focuses on improving efficiency, particularly through optimized sampling techniques like those leveraging truncated signed distance fields (TSDFs), and addressing inherent ambiguities in inverse rendering by incorporating prior knowledge about scene properties. These methods, often employing neural networks with microfacet reflectance models, are finding applications in diverse fields such as avatar creation, virtual try-on, and computer graphics, offering significant advancements in realistic 3D scene reconstruction from limited input data.