Search Results for author: Peter Hedman

Found 8 papers, 6 papers with code

Ref-NeRF: Structured View-Dependent Appearance for Neural Radiance Fields

1 code implementation CVPR 2022 Dor Verbin, Peter Hedman, Ben Mildenhall, Todd Zickler, Jonathan T. Barron, Pratul P. Srinivasan

Neural Radiance Fields (NeRF) is a popular view synthesis technique that represents a scene as a continuous volumetric function, parameterized by multilayer perceptrons that provide the volume density and view-dependent emitted radiance at each location.

Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fields

1 code implementation CVPR 2022 Jonathan T. Barron, Ben Mildenhall, Dor Verbin, Pratul P. Srinivasan, Peter Hedman

Though neural radiance fields (NeRF) have demonstrated impressive view synthesis results on objects and small bounded regions of space, they struggle on "unbounded" scenes, where the camera may point in any direction and content may exist at any distance.

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

2 code implementations24 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 implementations ICCV 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

3 code implementations ICCV 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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