activity
20232025
most citedCamP: Camera Preconditioning for Neural Radiance Fields

1 citations · 2 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV20251 cited

CubeDiff: Repurposing Diffusion-Based Image Models for Panorama Generation

Nikolai Kalischek, Michael Oechsle, Fabian Manhardt +3

We introduce a novel method for generating 360° panoramas from text prompts or images. Our approach leverages recent advances in 3D generation by employing multi-view diffusion mod…

cs.CV2024

Can Generative Video Models Help Pose Estimation?

Ruojin Cai, Jason Y. Zhang, Philipp Henzler +3

Pairwise pose estimation from images with little or no overlap is an open challenge in computer vision. Existing methods, even those trained on large-scale datasets, struggle in th…

cs.CV2024

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…

cs.CV2024

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…

cs.CV20231 cited

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…