Neural Volume Super-Resolution

Understand

Neural volumetric representations have become a widely adopted model for radiance fields in 3D scenes.

• These representations are fully implicit or hybrid function approximators of the instantaneous volumetric radiance in a scene, which are typically learned from multi-view captures of the scene.
• We investigate the new task of neural volume super-resolution - rendering high-resolution views corresponding to a scene captured at low resolution.
• To this end, we propose a neural super-resolution network that operates directly on the volumetric representation of the scene.