Learning Object-Centric Neural Scattering Functions for Free-Viewpoint Relighting and Scene Composition

Understand

Photorealistic object appearance modeling from 2D images is a constant topic in vision and graphics.

• While neural implicit methods (such as Neural Radiance Fields) have shown high-fidelity view synthesis results, they cannot relight the captured objects.
• More recent neural inverse rendering approaches have enabled object relighting, but they represent surface properties as simple BRDFs, and therefore cannot handle translucent objects.
• We propose Object-Centric Neural Scattering Functions (OSFs) for learning to reconstruct object appearance from only images.