1.2k citations · 2k across the 23 of their papers we have counts for
25 papers · 1 filter
Viser: Imperative, Web-based 3D Visualization in Python
Brent Yi, Chung Min Kim, Justin Kerr +8
We present Viser, a 3D visualization library for computer vision and robotics. Viser aims to bring easy and extensible 3D visualization to Python: we provide a comprehensive set of…
Decentralized Diffusion Models
David McAllister, Matthew Tancik, Jiaming Song +1
Large-scale AI model training divides work across thousands of GPUs, then synchronizes gradients across them at each step. This incurs a significant network burden that only centra…
gsplat: An Open-Source Library for Gaussian Splatting
Vickie Ye, Ruilong Li, Justin Kerr +8
gsplat is an open-source library designed for training and developing Gaussian Splatting methods. It features a front-end with Python bindings compatible with the PyTorch library a…
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…
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…
NerfAcc: A General NeRF Acceleration Toolbox
Ruilong Li, Matthew Tancik, Angjoo Kanazawa
We propose NerfAcc, a toolbox for efficient volumetric rendering of radiance fields. We build on the techniques proposed in Instant-NGP, and extend these techniques to not only sup…
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…
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…
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…
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…
Learned Initializations for Optimizing Coordinate-Based Neural Representations
Matthew Tancik, Ben Mildenhall, Terrance Wang +4
Coordinate-based neural representations have shown significant promise as an alternative to discrete, array-based representations for complex low dimensional signals. However, opti…
Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains
Matthew Tancik, Pratul P. Srinivasan, Ben Mildenhall +6
We show that passing input points through a simple Fourier feature mapping enables a multilayer perceptron (MLP) to learn high-frequency functions in low-dimensional problem domain…
Lighthouse: Predicting Lighting Volumes for Spatially-Coherent Illumination
Pratul P. Srinivasan, Ben Mildenhall, Matthew Tancik +3
We present a deep learning solution for estimating the incident illumination at any 3D location within a scene from an input narrow-baseline stereo image pair. Previous approaches…
StegaStamp: Invisible Hyperlinks in Physical Photographs
Matthew Tancik, Ben Mildenhall, Ren Ng
Printed and digitally displayed photos have the ability to hide imperceptible digital data that can be accessed through internet-connected imaging systems. Another way to think abo…
Flash Photography for Data-Driven Hidden Scene Recovery
Matthew Tancik, Guy Satat, Ramesh Raskar
Vehicles, search and rescue personnel, and endoscopes use flash lights to locate, identify, and view objects in their surroundings. Here we show the first steps of how all these ta…
Synthetically Trained Icon Proposals for Parsing and Summarizing Infographics
Spandan Madan, Zoya Bylinskii, Matthew Tancik +6
Widely used in news, business, and educational media, infographics are handcrafted to effectively communicate messages about complex and often abstract topics including `ways to co…