Neural Inverse Rendering

Neural inverse rendering aims to reconstruct 3D scene properties—geometry, materials, and lighting—from 2D images, reversing the rendering process. Current research focuses on improving the accuracy and efficiency of this reconstruction, often employing neural radiance fields, signed distance functions (SDFs), and various neural network architectures to model complex phenomena like reflections, shadows, and anisotropic materials. This field is significant for its potential applications in diverse areas, including computer graphics (e.g., novel view synthesis, relighting), robotics (e.g., 3D scene understanding), and medical imaging (e.g., intraoperative registration), by enabling more realistic and accurate 3D modeling from readily available image data.