Search Results for author: Peter Hedman

Found 4 papers, 3 papers with code

HyperNeRF: A Higher-Dimensional Representation for Topologically Varying Neural Radiance Fields

1 code implementation24 Jun 2021 Keunhong Park, Utkarsh Sinha, Peter Hedman, Jonathan T. Barron, Sofien Bouaziz, Dan B Goldman, Ricardo Martin-Brualla, Steven M. Seitz

A common approach to reconstruct such non-rigid scenes is through the use of a learned deformation field mapping from coordinates in each input image into a canonical template coordinate space.

Novel View Synthesis

Baking Neural Radiance Fields for Real-Time View Synthesis

no code implementations26 Mar 2021 Peter Hedman, Pratul P. Srinivasan, Ben Mildenhall, Jonathan T. Barron, Paul Debevec

Neural volumetric representations such as Neural Radiance Fields (NeRF) have emerged as a compelling technique for learning to represent 3D scenes from images with the goal of rendering photorealistic images of the scene from unobserved viewpoints.

Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fields

1 code implementation24 Mar 2021 Jonathan T. Barron, Ben Mildenhall, Matthew Tancik, Peter Hedman, Ricardo Martin-Brualla, Pratul P. Srinivasan

Mip-NeRF is also able to match the accuracy of a brute-force supersampled NeRF on our multiscale dataset while being 22x faster.

Deep Blending for Free-Viewpoint Image-Based-Rendering

1 code implementation SIGGRAPH Asia 2018 2018 Peter Hedman, Julien Philip, True Price, Jan-Michael Frahm, George Drettakis, Gabriel Brostow

We present a new deep learning approach to blending for IBR, in which we use held-out real image data to learn blending weights to combine input photo contributions.

Novel View Synthesis

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