20 citations · 72 across the 25 of their papers we have counts for
27 papers
Eulerian Gaussian Splatting using Hashed Probability Pyramids
Mia Gaia Polansky, George Kopanas, Stephan Garbin +2
We introduce a probabilistic splat-based radiance field framework that retains the fast rasterization and test-time efficiency of 3D Gaussian Splatting (3DGS) while replacing heuri…
Fourier Feature Pyramids for Physics-Informed Neural Networks
Brandon Zhao, Yixuan Wang, Jonathan T. Barron +3
We present an improved neural field architecture for solving partial differential equations (PDEs). Current physics-informed neural networks (PINNs) provide a flexible framework fo…
Power Foam: Unifying Real-Time Differentiable Ray Tracing and Rasterization
Shrisudhan Govindarajan, Daniel Rebain, Dor Verbin +3
We introduce a differentiable 3D representation that unifies the ray tracing capabilities of foam-based ray tracing with the efficiency of modern rasterization pipelines. While pri…
GR3EN: Generative Relighting for 3D Environments
Xiaoyan Xing, Philipp Henzler, Junhwa Hur +4
We present a method for relighting 3D reconstructions of large room-scale environments. Existing solutions for 3D scene relighting often require solving under-determined or ill-con…
Spherical Voronoi: Directional Appearance as a Differentiable Partition of the Sphere
Francesco Di Sario, Daniel Rebain, Dor Verbin +2
Radiance field methods (e.g. 3D Gaussian Splatting) have emerged as a powerful paradigm for novel view synthesis, yet their appearance modeling often relies on Spherical Harmonics…
ROGR: Relightable 3D Objects using Generative Relighting
Jiapeng Tang, Matthew Levine, Dor Verbin +5
We introduce ROGR, a novel approach that reconstructs a relightable 3D model of an object captured from multiple views, driven by a generative relighting model that simulates the e…
Generative Multiview Relighting for 3D Reconstruction under Extreme Illumination Variation
Hadi Alzayer, Philipp Henzler, Jonathan T. Barron +3
Reconstructing the geometry and appearance of objects from photographs taken in different environments is difficult as the illumination and therefore the object appearance vary acr…
SimVS: Simulating World Inconsistencies for Robust View Synthesis
Alex Trevithick, Roni Paiss, Philipp Henzler +9
Novel-view synthesis techniques achieve impressive results for static scenes but struggle when faced with the inconsistencies inherent to casual capture settings: varying illuminat…
EVER: Exact Volumetric Ellipsoid Rendering for Real-time View Synthesis
Alexander Mai, Peter Hedman, George Kopanas +6
We present Exact Volumetric Ellipsoid Rendering (EVER), a method for real-time differentiable emission-only volume rendering. Unlike recent rasterization based approach by 3D Gauss…
Flash Cache: Reducing Bias in Radiance Cache Based Inverse Rendering
Benjamin Attal, Dor Verbin, Ben Mildenhall +4
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…
IllumiNeRF: 3D Relighting Without Inverse Rendering
Xiaoming Zhao, Pratul P. Srinivasan, Dor Verbin +3
Existing methods for relightable view synthesis -- using a set of images of an object under unknown lighting to recover a 3D representation that can be rendered from novel viewpoin…
NeRF-Casting: Improved View-Dependent Appearance with Consistent Reflections
Dor Verbin, Pratul P. Srinivasan, Peter Hedman +4
Neural Radiance Fields (NeRFs) typically struggle to reconstruct and render highly specular objects, whose appearance varies quickly with changes in viewpoint. Recent works have im…
Binary Opacity Grids: Capturing Fine Geometric Detail for Mesh-Based View Synthesis
Christian Reiser, Stephan Garbin, Pratul P. Srinivasan +6
While surface-based view synthesis algorithms are appealing due to their low computational requirements, they often struggle to reproduce thin structures. In contrast, more expensi…
Boundary Attention: Learning curves, corners, junctions and grouping
Mia Gaia Polansky, Charles Herrmann, Junhwa Hur +3
We present a lightweight network that infers grouping and boundaries, including curves, corners and junctions. It operates in a bottom-up fashion, analogous to classical methods fo…
Nuvo: Neural UV Mapping for Unruly 3D Representations
Pratul P. Srinivasan, Stephan J. Garbin, Dor Verbin +2
Existing UV mapping algorithms are designed to operate on well-behaved meshes, instead of the geometry representations produced by state-of-the-art 3D reconstruction and generation…
ReconFusion: 3D Reconstruction with Diffusion Priors
Rundi Wu, Ben Mildenhall, Philipp Henzler +8
3D reconstruction methods such as Neural Radiance Fields (NeRFs) excel at rendering photorealistic novel views of complex scenes. However, recovering a high-quality NeRF typically…
Generative Powers of Ten
Xiaojuan Wang, Janne Kontkanen, Brian Curless +6
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…
Eclipse: Disambiguating Illumination and Materials using Unintended Shadows
Dor Verbin, Ben Mildenhall, Peter Hedman +3
Decomposing an object's appearance into representations of its materials and the surrounding illumination is difficult, even when the object's 3D shape is known beforehand. This pr…
Zip-NeRF: Anti-Aliased Grid-Based Neural Radiance Fields
Jonathan T. Barron, Ben Mildenhall, Dor Verbin +2
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.…
Neural Microfacet Fields for Inverse Rendering
Alexander Mai, Dor Verbin, Falko Kuester +1
We present Neural Microfacet Fields, a method for recovering materials, geometry, and environment illumination from images of a scene. Our method uses a microfacet reflectance mode…
MERF: Memory-Efficient Radiance Fields for Real-time View Synthesis in Unbounded Scenes
Christian Reiser, Richard Szeliski, Dor Verbin +5
Neural radiance fields enable state-of-the-art photorealistic view synthesis. However, existing radiance field representations are either too compute-intensive for real-time render…
BakedSDF: Meshing Neural SDFs for Real-Time View Synthesis
Lior Yariv, Peter Hedman, Christian Reiser +5
We present a method for reconstructing high-quality meshes of large unbounded real-world scenes suitable for photorealistic novel view synthesis. We first optimize a hybrid neural…
Ref-NeRF: Structured View-Dependent Appearance for Neural Radiance Fields
Dor Verbin, Peter Hedman, Ben Mildenhall +3
Neural Radiance Fields (NeRF) is a popular view synthesis technique that represents a scene as a continuous volumetric function, parameterized by multilayer perceptrons that provid…
Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fields
Jonathan T. Barron, Ben Mildenhall, Dor Verbin +2
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 t…