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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…
Single View Refractive Index Tomography with Neural Fields
Brandon Zhao, Aviad Levis, Liam Connor +2
Refractive Index Tomography is the inverse problem of reconstructing the continuously-varying 3D refractive index in a scene using 2D projected image measurements. Although a purel…
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.…