Search Results for author:
Found 23 papers, 18 papers with code
GARField: Group Anything with Radiance Fields
We optimize this field from a set of 2D masks provided by Segment Anything (SAM) in a way that respects coarse-to-fine hierarchy, using scale to consistently fuse conflicting masks from different viewpoints.
NerfAcc: Efficient Sampling Accelerates NeRFs
Optimizing and rendering Neural Radiance Fields is computationally expensive due to the vast number of samples required by volume rendering.
Nerfbusters: Removing Ghostly Artifacts from Casually Captured NeRFs
Casually captured Neural Radiance Fields (NeRFs) suffer from artifacts such as floaters or flawed geometry when rendered outside the camera trajectory.
Instruct-NeRF2NeRF: Editing 3D Scenes with Instructions
We propose a method for editing NeRF scenes with text-instructions.
LERF: Language Embedded Radiance Fields
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, or actionable affordances.
Nerfstudio: A Modular Framework for Neural Radiance Field Development
Neural Radiance Fields (NeRF) are a rapidly growing area of research with wide-ranging applications in computer vision, graphics, robotics, and more.
NerfAcc: A General NeRF Acceleration Toolbox
We propose NerfAcc, a toolbox for efficient volumetric rendering of radiance fields.
The One Where They Reconstructed 3D Humans and Environments in TV Shows
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.
Block-NeRF: Scalable Large Scene Neural View Synthesis
We present Block-NeRF, a variant of Neural Radiance Fields that can represent large-scale environments.
Plenoxels: Radiance Fields without Neural Networks
We introduce Plenoxels (plenoptic voxels), a system for photorealistic view synthesis.
Putting NeRF on a Diet: Semantically Consistent Few-Shot View Synthesis
We present DietNeRF, a 3D neural scene representation estimated from a few images.
PlenOctrees for Real-time Rendering of Neural Radiance Fields
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.
Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fields
Mip-NeRF is also able to match the accuracy of a brute-force supersampled NeRF on our multiscale dataset while being 22x faster.
NeRV: Neural Reflectance and Visibility Fields for Relighting and View Synthesis
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 novel viewpoints under arbitrary lighting conditions.
Learned Initializations for Optimizing Coordinate-Based Neural Representations
Coordinate-based neural representations have shown significant promise as an alternative to discrete, array-based representations for complex low dimensional signals.
pixelNeRF: Neural Radiance Fields from One or Few Images
This allows the network to be trained across multiple scenes to learn a scene prior, enabling it to perform novel view synthesis in a feed-forward manner from a sparse set of views (as few as one).
Ranked #2 on
Generalizable Novel View Synthesis
on NERDS 360
Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains
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 domains.
NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis
Our algorithm represents a scene using a fully-connected (non-convolutional) deep network, whose input is a single continuous 5D coordinate (spatial location $(x, y, z)$ and viewing direction $(\theta, \phi)$) and whose output is the volume density and view-dependent emitted radiance at that spatial location.
Ranked #3 on
Generalizable Novel View Synthesis
on NERDS 360
Generalizable Novel View Synthesis
Low-Dose X-Ray Ct Reconstruction
+2
Lighthouse: Predicting Lighting Volumes for Spatially-Coherent Illumination
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.
StegaStamp: Invisible Hyperlinks in Physical Photographs
Printed and digitally displayed photos have the ability to hide imperceptible digital data that can be accessed through internet-connected imaging systems.
Flash Photography for Data-Driven Hidden Scene Recovery
The method is able to localize 12cm wide hidden objects in 2D with 1. 7cm accuracy.
Synthetically Trained Icon Proposals for Parsing and Summarizing Infographics
While automatic text extraction works well on infographics, computer vision approaches trained on natural images fail to identify the stand-alone visual elements in infographics, or `icons'.
Lensless Imaging with Compressive Ultrafast Sensing
Each sensor acquisition is encoded with a different illumination pattern and produces a time series where time is a function of the photon's origin in the scene.
