NeRF-Casting: Improved View-Dependent Appearance with Consistent Reflections
This addresses the challenge of rendering realistic reflections in NeRFs for computer graphics and vision applications, representing an incremental improvement over existing methods.
The paper tackles the problem of Neural Radiance Fields (NeRFs) struggling with specular objects and inconsistent reflections, resulting in a method that outperforms prior techniques for view synthesis of shiny scenes and achieves photorealistic specular appearance with comparable optimization time.
Neural Radiance Fields (NeRFs) typically struggle to reconstruct and render highly specular objects, whose appearance varies quickly with changes in viewpoint. Recent works have improved NeRF's ability to render detailed specular appearance of distant environment illumination, but are unable to synthesize consistent reflections of closer content. Moreover, these techniques rely on large computationally-expensive neural networks to model outgoing radiance, which severely limits optimization and rendering speed. We address these issues with an approach based on ray tracing: instead of querying an expensive neural network for the outgoing view-dependent radiance at points along each camera ray, our model casts reflection rays from these points and traces them through the NeRF representation to render feature vectors which are decoded into color using a small inexpensive network. We demonstrate that our model outperforms prior methods for view synthesis of scenes containing shiny objects, and that it is the only existing NeRF method that can synthesize photorealistic specular appearance and reflections in real-world scenes, while requiring comparable optimization time to current state-of-the-art view synthesis models.