NeRFiller

NeRFiller represents a breakthrough in the synthesis of multiview-consistent content for Neural Radiance Fields (NeRFs) and 3D Gaussian Splatting. Developed initially as research from UC Berkeley and integrated into the Blender ecosystem via the NerfStudio framework, NeRFiller addresses the chronic 'missing data' problem in 3D scans. By leveraging 2D generative diffusion priors (such as Stable Diffusion) and a novel joint-synthesis optimization loop, it fills 'holes' or occluded regions in a 3D scene while ensuring that the hallucinated geometry and texture remain stable across all possible camera angles. In the 2026 market, NeRFiller is a foundational tool for digital twin reconstruction and architectural visualization, allowing artists to remove unwanted objects or repair sparse captures without requiring re-shoots. Its architecture utilizes a 'Grid-of-Images' approach to maintain spatial coherence, making it technically superior to naive frame-by-frame inpainting methods. As Blender's volumetric capabilities have expanded, NeRFiller serves as the primary bridge for professional VFX workflows seeking to merge generative AI with physically accurate 3D environments.