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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…
State of the Art on Diffusion Models for Visual Computing
Ryan Po, Wang Yifan, Vladislav Golyanik +15
The field of visual computing is rapidly advancing due to the emergence of generative artificial intelligence (AI), which unlocks unprecedented capabilities for the generation, edi…
CamP: Camera Preconditioning for Neural Radiance Fields
Keunhong Park, Philipp Henzler, Ben Mildenhall +2
Neural Radiance Fields (NeRF) can be optimized to obtain high-fidelity 3D scene reconstructions of objects and large-scale scenes. However, NeRFs require accurate camera parameters…
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…
Learning a Diffusion Prior for NeRFs
Guandao Yang, Abhijit Kundu, Leonidas J. Guibas +2
Neural Radiance Fields (NeRFs) have emerged as a powerful neural 3D representation for objects and scenes derived from 2D data. Generating NeRFs, however, remains difficult in many…