Neural Rendering Method

Neural rendering methods aim to synthesize realistic images and videos of 3D scenes from input images or other data, enabling novel view generation and scene manipulation. Current research focuses on improving robustness to imperfect input data (e.g., low light, motion blur), handling complex light transport phenomena (e.g., subsurface scattering, reflections), and scaling to large scenes (e.g., entire cities). These advancements are significant for applications in autonomous driving, augmented reality, cultural heritage preservation, and other fields requiring high-fidelity 3D scene reconstruction and rendering. Key approaches involve neural radiance fields (NeRFs) and their variants, often combined with mesh-based or point-cloud representations to enhance efficiency and editing capabilities.