1.2k citations · 2k across the 23 of their papers we have counts for
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NerfAcc: Efficient Sampling Accelerates NeRFs
Ruilong Li, Hang Gao, Matthew Tancik +1
Optimizing and rendering Neural Radiance Fields is computationally expensive due to the vast number of samples required by volume rendering. Recent works have included alternative…
Nerfbusters: Removing Ghostly Artifacts from Casually Captured NeRFs
Frederik Warburg, Ethan Weber, Matthew Tancik +2
Casually captured Neural Radiance Fields (NeRFs) suffer from artifacts such as floaters or flawed geometry when rendered outside the camera trajectory. Existing evaluation protocol…
LERF: Language Embedded Radiance Fields
Justin Kerr, Chung Min Kim, Ken Goldberg +2
Humans describe the physical world using natural language to refer to specific 3D locations based on a vast range of properties: visual appearance, semantics, abstract associations…
Instruct-NeRF2NeRF: Editing 3D Scenes with Instructions
Ayaan Haque, Matthew Tancik, Alexei A. Efros +2
We propose a method for editing NeRF scenes with text-instructions. Given a NeRF of a scene and the collection of images used to reconstruct it, our method uses an image-conditione…
Nerfstudio: A Modular Framework for Neural Radiance Field Development
Matthew Tancik, Ethan Weber, Evonne Ng +10
Neural Radiance Fields (NeRF) are a rapidly growing area of research with wide-ranging applications in computer vision, graphics, robotics, and more. In order to streamline the dev…