Zobrazeno 1 - 10
of 209
pro vyhledávání: '"Ben Mildenhall"'
Autor:
Yifan Jiang, Bartlomiej Wronski, Ben Mildenhall, Jonathan T. Barron, Zhangyang Wang, Tianfan Xue
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783031197963
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3269b0962c8e983aaa34a44feb1ef85d
https://doi.org/10.1007/978-3-031-19797-0_25
https://doi.org/10.1007/978-3-031-19797-0_25
Autor:
Stephen Lombardi, M. Guo, Ayush Tewari, Sergio Orts-Escolano, Tomas Simon, Christian Theobalt, Lingjie Liu, Sean Fanello, Matthias Nießner, Gordon Wetzstein, Jun-Yan Zhu, Pratul P. Srinivasan, Maneesh Agrawala, Edgar Tretschk, Vincent Sitzmann, Zexiang Xu, Michael Zollhöfer, Ohad Fried, Justus Thies, Ben Mildenhall, Dan B. Goldman, Rohit Pandey
Publikováno v:
SIGGRAPH Courses
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0e0396c379064b1ba3a769c6186fd172
https://doi.org/10.1145/3450508.3464573
https://doi.org/10.1145/3450508.3464573
Autor:
Ren Ng, Matthew Tancik, Jonathan T. Barron, Ben Mildenhall, Pratul P. Srinivasan, Divi Schmidt, Terrance Wang
Publikováno v:
CVPR
Coordinate-based neural representations have shown significant promise as an alternative to discrete, array-based representations for complex low dimensional signals. However, optimizing a coordinate-based network from randomly initialized weights fo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2e2e0427ea5f23812b9a0c4f550b9b6d
https://doi.org/10.1109/cvpr46437.2021.00287
https://doi.org/10.1109/cvpr46437.2021.00287
Autor:
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 locat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::614b56b1ae0fa0c39e14aa778c04ddbe
Autor:
Matthew Tancik, Noah Snavely, Jonathan T. Barron, Ben Mildenhall, Pratul P. Srinivasan, Richard Tucker
Publikováno v:
CVPR
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. Previous approaches for predicting global illumination from images either predict just a s
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e29a376f070e308e6161c57796219090
Autor:
Ben Mildenhall, Matthew Tancik, Pratul P. Srinivasan, Ren Ng, Jonathan T. Barron, Ravi Ramamoorthi
Publikováno v:
Computer Vision – ECCV 2020 ISBN: 9783030584511
ECCV (1)
ECCV (1)
We present a method that achieves state-of-the-art results for synthesizing novel views of complex scenes by optimizing an underlying continuous volumetric scene function using a sparse set of input views. Our algorithm represents a scene using a ful
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a33bbdc3d337d024b4796e8d5303b4a0
https://doi.org/10.1007/978-3-030-58452-8_24
https://doi.org/10.1007/978-3-030-58452-8_24
Autor:
Stephen DiVerdi, Zexiang Xu, Kai-En Lin, Ravi Ramamoorthi, Ben Mildenhall, Kalyan Sunkavalli, Qi Sun, Yannick Hold-Geoffroy, Pratul P. Srinivasan
Publikováno v:
Computer Vision – ECCV 2020 ISBN: 9783030586003
ECCV (13)
ECCV (13)
We propose a learning-based approach for novel view synthesis for multi-camera 360\(^\circ \) panorama capture rigs. Previous work constructs RGBD panoramas from such data, allowing for view synthesis with small amounts of translation, but cannot han
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6876aa5b1f88d70cd38b63b34dbcaab1
https://doi.org/10.1007/978-3-030-58601-0_20
https://doi.org/10.1007/978-3-030-58601-0_20
Publikováno v:
CVPR
Machine learning techniques work best when the data used for training resembles the data used for evaluation. This holds true for learned single-image denoising algorithms, which are applied to real raw camera sensor readings but, due to practical co
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bc93a937c9495780a4abd2d34ba1e7e6
https://doi.org/10.1109/cvpr.2019.01129
https://doi.org/10.1109/cvpr.2019.01129
Publikováno v:
CVPR
Printed and digitally displayed photos have the ability to hide imperceptible digital data that can be accessed through internet-connected imaging systems. Another way to think about this is physical photographs that have unique QR codes invisibly em
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::34ee202ed78eb92816f37966f272b19f
Publikováno v:
CVPR
We present a technique for jointly denoising bursts of images taken from a handheld camera. In particular, we propose a convolutional neural network architecture for predicting spatially varying kernels that can both align and denoise frames, a synth
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::af2f4c4cde15d4d55c7d8d69d4b4698b
https://doi.org/10.1109/cvpr.2018.00265
https://doi.org/10.1109/cvpr.2018.00265