1.2k citations · 1.3k across the 5 of their papers we have counts for
12 papers
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…
Putting NeRF on a Diet: Semantically Consistent Few-Shot View Synthesis
Ajay Jain, Matthew Tancik, Pieter Abbeel
We present DietNeRF, a 3D neural scene representation estimated from a few images. Neural Radiance Fields (NeRF) learn a continuous volumetric representation of a scene through mul…
PlenOctrees for Real-time Rendering of Neural Radiance Fields
Alex Yu, Ruilong Li, Matthew Tancik +3
We introduce a method to render Neural Radiance Fields (NeRFs) in real time using PlenOctrees, an octree-based 3D representation which supports view-dependent effects. Our method c…
Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fields
Jonathan T. Barron, Ben Mildenhall, Matthew Tancik +3
The rendering procedure used by neural radiance fields (NeRF) samples a scene with a single ray per pixel and may therefore produce renderings that are excessively blurred or alias…
NeRV: Neural Reflectance and Visibility Fields for Relighting and View Synthesis
Pratul P. Srinivasan, Boyang Deng, Xiuming Zhang +3
We present a method that takes as input a set of images of a scene illuminated by unconstrained known lighting, and produces as output a 3D representation that can be rendered from…
pixelNeRF: Neural Radiance Fields from One or Few Images
Alex Yu, Vickie Ye, Matthew Tancik +1
We propose pixelNeRF, a learning framework that predicts a continuous neural scene representation conditioned on one or few input images. The existing approach for constructing neu…