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Learned Initializations for Optimizing Coordinate-Based Neural Representations
Matthew Tancik* ,
Ben Mildenhall* ,
Terrance Wang ,
Divi Schmidt ,
Pratul Srinivasan ,
Jonathan T. Barron ,
Ren Ng
arXiv , 2020
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arXiv
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We use meta-learning to find weight initializations for coordinate-based MLPs that allow them to converge faster and generalize better.
NeRV: Neural Reflectance and Visibility Fields for Relighting and View Synthesis
Pratul Srinivasan ,
Boyang Deng ,
Xiuming Zhang ,
Matthew Tancik ,
Ben Mildenhall ,
Jonathan T. Barron
arXiv , 2020
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We recover relightable NeRF-like models using neural approximations of expensive visibility integrals, so we can simulate complex volumetric light transport during training.
Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains
Matthew Tancik* ,
Pratul Srinivasan* ,
Ben Mildenhall* ,
Sara Fridovich-Keil ,
Nithin Raghavan ,
Utkarsh Singhal ,
Ravi Ramamoorthi ,
Jonathan T. Barron ,
Ren Ng
NeurIPS , 2020 (spotlight)
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arXiv
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We demonstrate that composing fully-connected networks with a simple Fourier feature mapping allows them to learn much high frequency functions.
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Neural Reflectance Fields for Appearance Acquisition
Sai Bi* ,
Zexiang Xu* ,
Pratul Srinivasan ,
Ben Mildenhall ,
Kalyan Sunkavalli ,
Milos Hasan ,
Yannick Hold-Geoffroy ,
David Kriegman ,
Ravi Ramamoorthi
arXiv , 2020
arXiv
We recover relightable NeRF-like models by predicting per-location BRDFs and surface normals, and marching light rays through the NeRV volume to compute visibility.
NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis
Ben Mildenhall* ,
Pratul Srinivasan* ,
Matthew Tancik* ,
Jonathan T. Barron ,
Ravi Ramamoorthi ,
Ren Ng
European Conference on Computer Vision (ECCV) , 2020 (Best Paper Honorable Mention)
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We optimize a simple fully-connected network to represent a single scene as a volume, then use volume rendering to do view synthesis.
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Deep Multi Depth Panoramas for View Synthesis
Kai-En Lin ,
Zexiang Xu ,
Ben Mildenhall ,
Pratul Srinivasan ,
Yannick Hold-Geoffroy ,
Stephen DiVerdi ,
Qi Sun ,
Kalyan Sunkavalli ,
Ravi Ramamoorthi
European Conference on Computer Vision (ECCV) , 2020
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We represent scenes as multi-layer panoramas with depth for VR view synthesis.
Lighthouse: Predicting Lighting Volumes for Spatially-Coherent Illumination
Pratul Srinivasan* ,
Ben Mildenhall* ,
Matthew Tancik ,
Jonathan T. Barron ,
Richard Tucker ,
Noah Snavely
Computer Vision and Pattern Recognition (CVPR) , 2020
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We predict a volume from an input stereo pair that can be used to calculate incident lighting at any 3D point within a scene.
StegaStamp: Invisible Hyperlinks in Physical Photographs
Matthew Tancik* ,
Ben Mildenhall* ,
Ren Ng
Computer Vision and Pattern Recognition (CVPR) , 2020
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arXiv
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We can hide hyperlinks in natural images to create aesthetically pleasing barcodes.
Local Light Field Fusion: Practical View Synthesis with Prescriptive Sampling Guidelines
Ben Mildenhall* ,
Pratul Srinivasan* ,
Rodrigo Ortiz-Cayon ,
Nima Khademi Kalantari ,
Ravi Ramamoorthi ,
Ren Ng ,
Abhishek Kar
SIGGRAPH , 2019
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arXiv
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We develop and analyze a deep learning method for rendering novel views of complex real world scenes.
Unprocessing Images for Learned Raw Denoising
Tim Brooks ,
Ben Mildenhall ,
Tianfan Xue ,
Jiawen Chen ,
Dillon Sharlet ,
Jonathan T. Barron
Computer Vision and Pattern Recognition (CVPR) , 2019 (oral)
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arXiv
We can learn a better denoising model by processing and unprocessing images the same way a camera does.
Burst Denoising with Kernel Prediction Networks
Ben Mildenhall ,
Jonathan T. Barron ,
Jiawen Chen ,
Dillon Sharlet,
Ren Ng ,
Robert Carroll
Computer Vision and Pattern Recognition (CVPR) , 2018 (spotlight)
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arXiv
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We train a network to predict linear kernels that denoise bursts of raw linear images.
DiffuserCam: Lensless Single-exposure 3D Imaging
Nick Antipa ,
Grace Kuo ,
Reinhard Heckel ,
Ben Mildenhall ,
Emrah Bostan ,
Ren Ng ,
Laura Waller
Optica , 2018
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arXiv
Using a diffuser instead of a lens lets you recover 3D in a single exposure.
Approximations for the distribution of microflake normals
Nelson Max ,
Tom Duff,
Ben Mildenhall ,
Yajie Yan
The Visual Computer , 2017
We precompute microflake approximations to make rendering large meshes at a distance more efficient.
Controlling Procedural Modeling Programs with Stochastically-Ordered Sequential Monte Carlo
Daniel Ritchie ,
Ben Mildenhall ,
Noah D. Goodman ,
Pat Hanrahan
SIGGRAPH , 2015
We improve control over the output of highly-variable procedural modeling programs by using SOSMC to provide incremental feedback on partially-generated models.
Yep it's another Jon Barron website.
Last updated January 2021.