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.
Simultaneously optimizes multiple viewpoints using a shared latent space to ensure zero 'popping' or flickering between frames.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Organizes multiple scene views into a single grid for batch processing through 2D diffusion models.
Integrates depth maps to ensure the inpainted area aligns with the existing 3D geometry of the scene.
Full compatibility with the industry-standard NerfStudio ecosystem for seamless training and rendering.
Uses high-fidelity 'anchor' frames to guide the inpainting process in areas with sparse data.
Propagates a 2D mask from one frame across the entire 3D volume using the NeRF's density field.
Tiled rendering and optimization techniques to allow processing on consumer-grade hardware (RTX 3090/4090).
Digital twin scans of historical sites often have missing sections due to limited physical access or occlusions from scaffolding.
Registry Updated:2/7/2026
Export clean 3DGS for VR tour
Removing moving cars or pedestrians from a 360-degree street-view scan.
Fixing the 'base' of a product that couldn't be photographed due to the surface it was sitting on.