Search Results for author: Ben Mildenhall

Found 23 papers, 12 papers with code

Fast and High-Quality Image Denoising via Malleable Convolutions

no code implementations2 Jan 2022 Yifan Jiang, Bart Wronski, Ben Mildenhall, Jon Barron, Zhangyang Wang, Tianfan Xue

To achieve spatial-varying processing without significant overhead, we present Malleable Convolution (MalleConv), as an efficient variant of dynamic convolution.

Image Denoising Image Restoration

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

no code implementations7 Dec 2021 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.

Zero-Shot Text-Guided Object Generation with Dream Fields

1 code implementation2 Dec 2021 Ajay Jain, Ben Mildenhall, Jonathan T. Barron, Pieter Abbeel, Ben Poole

Our method, Dream Fields, can generate the geometry and color of a wide range of objects without 3D supervision.

Neural Rendering

RegNeRF: Regularizing Neural Radiance Fields for View Synthesis from Sparse Inputs

no code implementations1 Dec 2021 Michael Niemeyer, Jonathan T. Barron, Ben Mildenhall, Mehdi S. M. Sajjadi, Andreas Geiger, Noha Radwan

We observe that the majority of artifacts in sparse input scenarios are caused by errors in the estimated scene geometry, and by divergent behavior at the start of training.

Novel View Synthesis

NeRF in the Dark: High Dynamic Range View Synthesis from Noisy Raw Images

no code implementations26 Nov 2021 Ben Mildenhall, Peter Hedman, Ricardo Martin-Brualla, Pratul Srinivasan, Jonathan T. Barron

By rendering raw output images from the resulting NeRF, we can perform novel high dynamic range (HDR) view synthesis tasks.

Novel View Synthesis

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

no code implementations23 Nov 2021 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.

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

2 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.

NeRV: Neural Reflectance and Visibility Fields for Relighting and View Synthesis

no code implementations CVPR 2021 Pratul P. Srinivasan, Boyang Deng, Xiuming Zhang, Matthew Tancik, Ben Mildenhall, Jonathan T. Barron

We present a method that takes as input a set of images of a scene illuminated by unconstrained known lighting, and produces as output a 3D representation that can be rendered from novel viewpoints under arbitrary lighting conditions.

Learned Initializations for Optimizing Coordinate-Based Neural Representations

2 code implementations CVPR 2021 Matthew Tancik, Ben Mildenhall, Terrance Wang, Divi Schmidt, Pratul P. Srinivasan, Jonathan T. Barron, Ren Ng

Coordinate-based neural representations have shown significant promise as an alternative to discrete, array-based representations for complex low dimensional signals.


Neural Reflectance Fields for Appearance Acquisition

no code implementations9 Aug 2020 Sai Bi, Zexiang Xu, Pratul Srinivasan, Ben Mildenhall, Kalyan Sunkavalli, Miloš Hašan, Yannick Hold-Geoffroy, David Kriegman, Ravi Ramamoorthi

We combine this representation with a physically-based differentiable ray marching framework that can render images from a neural reflectance field under any viewpoint and light.

Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains

9 code implementations NeurIPS 2020 Matthew Tancik, Pratul P. Srinivasan, Ben Mildenhall, Sara Fridovich-Keil, Nithin Raghavan, Utkarsh Singhal, Ravi Ramamoorthi, Jonathan T. Barron, Ren Ng

We show that passing input points through a simple Fourier feature mapping enables a multilayer perceptron (MLP) to learn high-frequency functions in low-dimensional problem domains.

NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis

26 code implementations ECCV 2020 Ben Mildenhall, Pratul P. Srinivasan, Matthew Tancik, Jonathan T. Barron, Ravi Ramamoorthi, Ren Ng

Our algorithm represents a scene using a fully-connected (non-convolutional) deep network, whose input is a single continuous 5D coordinate (spatial location $(x, y, z)$ and viewing direction $(\theta, \phi)$) and whose output is the volume density and view-dependent emitted radiance at that spatial location.

Neural Rendering Novel View Synthesis

Lighthouse: Predicting Lighting Volumes for Spatially-Coherent Illumination

1 code implementation CVPR 2020 Pratul P. Srinivasan, Ben Mildenhall, Matthew Tancik, Jonathan T. Barron, Richard Tucker, Noah Snavely

We present a deep learning solution for estimating the incident illumination at any 3D location within a scene from an input narrow-baseline stereo image pair.

Local Light Field Fusion: Practical View Synthesis with Prescriptive Sampling Guidelines

1 code implementation2 May 2019 Ben Mildenhall, Pratul P. Srinivasan, Rodrigo Ortiz-Cayon, Nima Khademi Kalantari, Ravi Ramamoorthi, Ren Ng, Abhishek Kar

We present a practical and robust deep learning solution for capturing and rendering novel views of complex real world scenes for virtual exploration.

Novel View Synthesis

StegaStamp: Invisible Hyperlinks in Physical Photographs

2 code implementations CVPR 2020 Matthew Tancik, Ben Mildenhall, Ren Ng

Printed and digitally displayed photos have the ability to hide imperceptible digital data that can be accessed through internet-connected imaging systems.


DiffuserCam: Lensless Single-exposure 3D Imaging

no code implementations5 Oct 2017 Nick Antipa, Grace Kuo, Reinhard Heckel, Ben Mildenhall, Emrah Bostan, Ren Ng, Laura Waller

We demonstrate a compact and easy-to-build computational camera for single-shot 3D imaging.

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