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Found 41 papers, 21 papers with code
Flash Cache: Reducing Bias in Radiance Cache Based Inverse Rendering
State-of-the-art techniques for 3D reconstruction are largely based on volumetric scene representations, which require sampling multiple points to compute the color arriving along a ray.
NeRF-Casting: Improved View-Dependent Appearance with Consistent Reflections
Neural Radiance Fields (NeRFs) typically struggle to reconstruct and render highly specular objects, whose appearance varies quickly with changes in viewpoint.
GaussianFlow: Splatting Gaussian Dynamics for 4D Content Creation
While the optimization can draw photometric reference from the input videos or be regulated by generative models, directly supervising Gaussian motions remains underexplored.
Disentangled 3D Scene Generation with Layout Learning
We introduce a method to generate 3D scenes that are disentangled into their component objects.
Binary Opacity Grids: Capturing Fine Geometric Detail for Mesh-Based View Synthesis
Third, we minimize the binary entropy of the opacity values, which facilitates the extraction of surface geometry by encouraging opacity values to binarize towards the end of training.
Nuvo: Neural UV Mapping for Unruly 3D Representations
We present a UV mapping method designed to operate on geometry produced by 3D reconstruction and generation techniques.
ReconFusion: 3D Reconstruction with Diffusion Priors
3D reconstruction methods such as Neural Radiance Fields (NeRFs) excel at rendering photorealistic novel views of complex scenes.
Generative Powers of Ten
We present a method that uses a text-to-image model to generate consistent content across multiple image scales, enabling extreme semantic zooms into a scene, e. g., ranging from a wide-angle landscape view of a forest to a macro shot of an insect sitting on one of the tree branches.
State of the Art on Diffusion Models for Visual Computing
The field of visual computing is rapidly advancing due to the emergence of generative artificial intelligence (AI), which unlocks unprecedented capabilities for the generation, editing, and reconstruction of images, videos, and 3D scenes.
CamP: Camera Preconditioning for Neural Radiance Fields
We propose using a proxy problem to compute a whitening transform that eliminates the correlation between camera parameters and normalizes their effects, and we propose to use this transform as a preconditioner for the camera parameters during joint optimization.
Eclipse: Disambiguating Illumination and Materials using Unintended Shadows
We present a method based on differentiable Monte Carlo ray tracing that uses images of an object to jointly recover its spatially-varying materials, the surrounding illumination environment, and the shapes of the unseen light occluders who inadvertently cast shadows upon it.
Zip-NeRF: Anti-Aliased Grid-Based Neural Radiance Fields
Neural Radiance Field training can be accelerated through the use of grid-based representations in NeRF's learned mapping from spatial coordinates to colors and volumetric density.
DreamBooth3D: Subject-Driven Text-to-3D Generation
We present DreamBooth3D, an approach to personalize text-to-3D generative models from as few as 3-6 casually captured images of a subject.
BakedSDF: Meshing Neural SDFs for Real-Time View Synthesis
We present a method for reconstructing high-quality meshes of large unbounded real-world scenes suitable for photorealistic novel view synthesis.
MERF: Memory-Efficient Radiance Fields for Real-time View Synthesis in Unbounded Scenes
We design a lossless procedure for baking the parameterization used during training into a model that achieves real-time rendering while still preserving the photorealistic view synthesis quality of a volumetric radiance field.
AligNeRF: High-Fidelity Neural Radiance Fields via Alignment-Aware Training
Neural Radiance Fields (NeRFs) are a powerful representation for modeling a 3D scene as a continuous function.
DreamFusion: Text-to-3D using 2D Diffusion
Using this loss in a DeepDream-like procedure, we optimize a randomly-initialized 3D model (a Neural Radiance Field, or NeRF) via gradient descent such that its 2D renderings from random angles achieve a low loss.
Ranked #5 on
Text to 3D
on T$^3$Bench
Volume Rendering Digest (for NeRF)
Neural Radiance Fields employ simple volume rendering as a way to overcome the challenges of differentiating through ray-triangle intersections by leveraging a probabilistic notion of visibility.
Block-NeRF: Scalable Large Scene Neural View Synthesis
We present Block-NeRF, a variant of Neural Radiance Fields that can represent large-scale environments.
Fast and High-Quality Image Denoising via Malleable Convolutions
These spatially-varying kernels are produced by an efficient predictor network running on a downsampled input, making them much more efficient to compute than per-pixel kernels produced by a full-resolution image, and also enlarging the network's receptive field compared with static kernels.
Ref-NeRF: Structured View-Dependent Appearance for Neural Radiance Fields
Neural Radiance Fields (NeRF) is a popular view synthesis technique that represents a scene as a continuous volumetric function, parameterized by multilayer perceptrons that provide the volume density and view-dependent emitted radiance at each location.
Dense Depth Priors for Neural Radiance Fields from Sparse Input Views
To this end, we leverage dense depth priors in order to constrain the NeRF optimization.
Zero-Shot Text-Guided Object Generation with Dream Fields
Our method, Dream Fields, can generate the geometry and color of a wide range of objects without 3D supervision.
RegNeRF: Regularizing Neural Radiance Fields for View Synthesis from Sparse Inputs
We observe that the majority of artifacts in sparse input scenarios are caused by errors in the estimated scene geometry, and by divergent behavior at the start of training.
NeRF in the Dark: High Dynamic Range View Synthesis from Noisy Raw Images
By rendering raw output images from the resulting NeRF, we can perform novel high dynamic range (HDR) view synthesis tasks.
Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fields
Though neural radiance fields (NeRF) have demonstrated impressive view synthesis results on objects and small bounded regions of space, they struggle on "unbounded" scenes, where the camera may point in any direction and content may exist at any distance.
Ranked #8 on
Novel View Synthesis
on Tanks and Temples
Advances in Neural Rendering
The reconstruction of such a scene representation from observations using differentiable rendering losses is known as inverse graphics or inverse rendering.
Baking Neural Radiance Fields for Real-Time View Synthesis
Neural volumetric representations such as Neural Radiance Fields (NeRF) have emerged as a compelling technique for learning to represent 3D scenes from images with the goal of rendering photorealistic images of the scene from unobserved viewpoints.
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.
Neural Reflectance Fields for Appearance Acquisition
We combine this representation with a physically-based differentiable ray marching framework that can render images from a neural reflectance field under any viewpoint and light.
Deep Multi Depth Panoramas for View Synthesis
We propose a learning-based approach for novel view synthesis for multi-camera 360$^{\circ}$ panorama capture rigs.
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.
Local Light Field Fusion: Practical View Synthesis with Prescriptive Sampling Guidelines
We present a practical and robust deep learning solution for capturing and rendering novel views of complex real world scenes for virtual exploration.
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.
Unprocessing Images for Learned Raw Denoising
Machine learning techniques work best when the data used for training resembles the data used for evaluation.
Ranked #1 on
Color Image Denoising
on Darmstadt Noise Dataset
Burst Denoising with Kernel Prediction Networks
We present a technique for jointly denoising bursts of images taken from a handheld camera.
DiffuserCam: Lensless Single-exposure 3D Imaging
We demonstrate a compact and easy-to-build computational camera for single-shot 3D imaging.
