NeRF
582 papers with code • 0 benchmarks • 0 datasets
Benchmarks
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Libraries
Use these libraries to find NeRF models and implementationsMost implemented papers
NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis
bmild/nerf
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ECCV 2020
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.
NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstruction
Totoro97/NeuS
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NeurIPS 2021
In NeuS, we propose to represent a surface as the zero-level set of a signed distance function (SDF) and develop a new volume rendering method to train a neural SDF representation.
NeRF++: Analyzing and Improving Neural Radiance Fields
Neural Radiance Fields (NeRF) achieve impressive view synthesis results for a variety of capture settings, including 360 capture of bounded scenes and forward-facing capture of bounded and unbounded scenes.
NeRF--: Neural Radiance Fields Without Known Camera Parameters
Considering the problem of novel view synthesis (NVS) from only a set of 2D images, we simplify the training process of Neural Radiance Field (NeRF) on forward-facing scenes by removing the requirement of known or pre-computed camera parameters, including both intrinsics and 6DoF poses.
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.
LERF: Language Embedded Radiance Fields
code-implementation1/Code4
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ICCV 2023
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.
DreamCatalyst: Fast and High-Quality 3D Editing via Controlling Editability and Identity Preservation
Score distillation sampling (SDS) has emerged as an effective framework in text-driven 3D editing tasks, leveraging diffusion models for 3D-consistent editing.
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.
KiloNeRF: Speeding up Neural Radiance Fields with Thousands of Tiny MLPs
creiser/kilonerf
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ICCV 2021
NeRF synthesizes novel views of a scene with unprecedented quality by fitting a neural radiance field to RGB images.
BARF: Bundle-Adjusting Neural Radiance Fields
chenhsuanlin/bundle-adjusting-NeRF
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ICCV 2021
In this paper, we propose Bundle-Adjusting Neural Radiance Fields (BARF) for training NeRF from imperfect (or even unknown) camera poses -- the joint problem of learning neural 3D representations and registering camera frames.
