1.2k citations · 3.8k across the 55 of their papers we have counts for
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AligNeRF: High-Fidelity Neural Radiance Fields via Alignment-Aware Training
Yifan Jiang, Peter Hedman, Ben Mildenhall +4
Neural Radiance Fields (NeRFs) are a powerful representation for modeling a 3D scene as a continuous function. Though NeRF is able to render complex 3D scenes with view-dependent e…
DreamFusion: Text-to-3D using 2D Diffusion
Ben Poole, Ajay Jain, Jonathan T. Barron +1
Recent breakthroughs in text-to-image synthesis have been driven by diffusion models trained on billions of image-text pairs. Adapting this approach to 3D synthesis would require l…
SAMURAI: Shape And Material from Unconstrained Real-world Arbitrary Image collections
Mark Boss, Andreas Engelhardt, Abhishek Kar +5
Inverse rendering of an object under entirely unknown capture conditions is a fundamental challenge in computer vision and graphics. Neural approaches such as NeRF have achieved ph…
NeRF-Supervision: Learning Dense Object Descriptors from Neural Radiance Fields
Lin Yen-Chen, Pete Florence, Jonathan T. Barron +3
Thin, reflective objects such as forks and whisks are common in our daily lives, but they are particularly challenging for robot perception because it is hard to reconstruct them u…
Block-NeRF: Scalable Large Scene Neural View Synthesis
Matthew Tancik, Vincent Casser, Xinchen Yan +5
We present Block-NeRF, a variant of Neural Radiance Fields that can represent large-scale environments. Specifically, we demonstrate that when scaling NeRF to render city-scale sce…
HumanNeRF: Free-viewpoint Rendering of Moving People from Monocular Video
Chung-Yi Weng, Brian Curless, Pratul P. Srinivasan +2
We introduce a free-viewpoint rendering method -- HumanNeRF -- that works on a given monocular video of a human performing complex body motions, e.g. a video from YouTube. Our meth…