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Found 27 papers, 14 papers with code
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.
PersonNeRF: Personalized Reconstruction from Photo Collections
We present PersonNeRF, a method that takes a collection of photos of a subject (e. g. Roger Federer) captured across multiple years with arbitrary body poses and appearances, and enables rendering the subject with arbitrary novel combinations of viewpoint, body pose, and appearance.
Gravitationally Lensed Black Hole Emission Tomography
In this work, we propose BH-NeRF, a novel tomography approach that leverages gravitational lensing to recover the continuous 3D emission field near a black hole.
Block-NeRF: Scalable Large Scene Neural View Synthesis
We present Block-NeRF, a variant of Neural Radiance Fields that can represent large-scale environments.
HumanNeRF: Free-viewpoint Rendering of Moving People from Monocular Video
Our method optimizes for a volumetric representation of the person in a canonical T-pose, in concert with a motion field that maps the estimated canonical representation to every frame of the video via backward warps.
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.
Urban Radiance Fields
The goal of this work is to perform 3D reconstruction and novel view synthesis from data captured by scanning platforms commonly deployed for world mapping in urban outdoor environments (e. g., Street View).
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.
Defocus Map Estimation and Deblurring from a Single Dual-Pixel Image
We use data captured with a consumer smartphone camera to demonstrate that, after a one-time calibration step, our approach improves upon prior works for both defocus map estimation and blur removal, despite being entirely unsupervised.
NeRFactor: Neural Factorization of Shape and Reflectance Under an Unknown Illumination
This enables the rendering of novel views of the object under arbitrary environment lighting and editing of the object's material properties.
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.
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.
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.
Pushing the Boundaries of View Extrapolation with Multiplane Images
We present a theoretical analysis showing how the range of views that can be rendered from an MPI increases linearly with the MPI disparity sampling frequency, as well as a novel MPI prediction procedure that theoretically enables view extrapolations of up to $4\times$ the lateral viewpoint movement allowed by prior work.
Aperture Supervision for Monocular Depth Estimation
We present a novel method to train machine learning algorithms to estimate scene depths from a single image, by using the information provided by a camera's aperture as supervision.
Learning to Synthesize a 4D RGBD Light Field from a Single Image
We present a machine learning algorithm that takes as input a 2D RGB image and synthesizes a 4D RGBD light field (color and depth of the scene in each ray direction).
Light Field Blind Motion Deblurring
We study the problem of deblurring light fields of general 3D scenes captured under 3D camera motion and present both theoretical and practical contributions.
Oriented Light-Field Windows for Scene Flow
2D spatial image windows are used for comparing pixel values in computer vision applications such as correspondence for optical flow and 3D reconstruction, bilateral filtering, and image segmentation.
Depth From Shading, Defocus, and Correspondence Using Light-Field Angular Coherence
Using shading information is essential to improve the shape estimation.
