5 citations · 12 across the 6 of their papers we have counts for
6 papers
GARField: Group Anything with Radiance Fields
Chung Min Kim, Mingxuan Wu, Justin Kerr +3
Grouping is inherently ambiguous due to the multiple levels of granularity in which one can decompose a scene -- should the wheels of an excavator be considered separate or part of…
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…
The One Where They Reconstructed 3D Humans and Environments in TV Shows
Georgios Pavlakos, Ethan Weber, Matthew Tancik +1
TV shows depict a wide variety of human behaviors and have been studied extensively for their potential to be a rich source of data for many applications. However, the majority of…
Plenoxels: Radiance Fields without Neural Networks
Alex Yu, Sara Fridovich-Keil, Matthew Tancik +3
We introduce Plenoxels (plenoptic voxels), a system for photorealistic view synthesis. Plenoxels represent a scene as a sparse 3D grid with spherical harmonics. This representation…
Lensless Imaging with Compressive Ultrafast Sensing
Guy Satat, Matthew Tancik, Ramesh Raskar
Lensless imaging is an important and challenging problem. One notable solution to lensless imaging is a single pixel camera which benefits from ideas central to compressive samplin…