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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…
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…
CAT3D: Create Anything in 3D with Multi-View Diffusion Models
Ruiqi Gao, Aleksander Holynski, Philipp Henzler +5
Advances in 3D reconstruction have enabled high-quality 3D capture, but require a user to collect hundreds to thousands of images to create a 3D scene. We present CAT3D, a method f…