Neural BRDF

Neural BRDFs leverage neural networks to represent and manipulate bidirectional reflectance distribution functions (BRDFs), aiming for efficient and realistic material rendering in computer graphics. Current research focuses on developing compact neural network architectures, such as those employing low-dimensional projections and learnable spherical primitives, to achieve real-time performance and high-fidelity material representation. These advancements enable applications like text-to-3D generation with accurate material modeling and facilitate operations such as BRDF layering and interpolation directly in a latent space, improving efficiency over traditional methods. The resulting improvements in rendering speed and material fidelity have significant implications for various fields, including computer graphics, virtual reality, and augmented reality.