Search Results for author: Johannes Kopf

Found 14 papers, 6 papers with code

Learning Neural Light Fields With Ray-Space Embedding

no code implementations CVPR 2022 Benjamin Attal, Jia-Bin Huang, Michael Zollhöfer, Johannes Kopf, Changil Kim

Our method supports rendering with a single network evaluation per pixel for small baseline light fields and with only a few evaluations per pixel for light fields with larger baselines.

Boosting View Synthesis With Residual Transfer

no code implementations CVPR 2022 Xuejian Rong, Jia-Bin Huang, Ayush Saraf, Changil Kim, Johannes Kopf

We present a simple but effective technique to boost the rendering quality, which can be easily integrated with most view synthesis methods.

Novel View Synthesis

Learning Neural Light Fields with Ray-Space Embedding Networks

1 code implementation2 Dec 2021 Benjamin Attal, Jia-Bin Huang, Michael Zollhoefer, Johannes Kopf, Changil Kim

Our method supports rendering with a single network evaluation per pixel for small baseline light field datasets and can also be applied to larger baselines with only a few evaluations per pixel.

Dynamic View Synthesis from Dynamic Monocular Video

no code implementations ICCV 2021 Chen Gao, Ayush Saraf, Johannes Kopf, Jia-Bin Huang

We present an algorithm for generating novel views at arbitrary viewpoints and any input time step given a monocular video of a dynamic scene.

Robust Consistent Video Depth Estimation

1 code implementation CVPR 2021 Johannes Kopf, Xuejian Rong, Jia-Bin Huang

We present an algorithm for estimating consistent dense depth maps and camera poses from a monocular video.

Depth Estimation

Space-time Neural Irradiance Fields for Free-Viewpoint Video

no code implementations CVPR 2021 Wenqi Xian, Jia-Bin Huang, Johannes Kopf, Changil Kim

We present a method that learns a spatiotemporal neural irradiance field for dynamic scenes from a single video.

Depth Estimation

One Shot 3D Photography

no code implementations27 Aug 2020 Johannes Kopf, Kevin Matzen, Suhib Alsisan, Ocean Quigley, Francis Ge, Yangming Chong, Josh Patterson, Jan-Michael Frahm, Shu Wu, Matthew Yu, Peizhao Zhang, Zijian He, Peter Vajda, Ayush Saraf, Michael Cohen

3D photos are static in time, like traditional photos, but are displayed with interactive parallax on mobile or desktop screens, as well as on Virtual Reality devices, where viewing it also includes stereo.

Monocular Depth Estimation

Consistent Video Depth Estimation

3 code implementations30 Apr 2020 Xuan Luo, Jia-Bin Huang, Richard Szeliski, Kevin Matzen, Johannes Kopf

We present an algorithm for reconstructing dense, geometrically consistent depth for all pixels in a monocular video.

Depth Estimation

3D Photography using Context-aware Layered Depth Inpainting

1 code implementation CVPR 2020 Meng-Li Shih, Shih-Yang Su, Johannes Kopf, Jia-Bin Huang

We propose a method for converting a single RGB-D input image into a 3D photo - a multi-layer representation for novel view synthesis that contains hallucinated color and depth structures in regions occluded in the original view.

Novel View Synthesis

DeepMVS: Learning Multi-view Stereopsis

1 code implementation CVPR 2018 Po-Han Huang, Kevin Matzen, Johannes Kopf, Narendra Ahuja, Jia-Bin Huang

We present DeepMVS, a deep convolutional neural network (ConvNet) for multi-view stereo reconstruction.

Co-segmentation for Space-Time Co-located Collections

no code implementations31 Jan 2017 Hadar Averbuch-Elor, Johannes Kopf, Tamir Hazan, Daniel Cohen-Or

Thus, to disambiguate what the common foreground object is, we introduce a weakly-supervised technique, where we assume only a small seed, given in the form of a single segmented image.

Virtual Rephotography: Novel View Prediction Error for 3D Reconstruction

no code implementations26 Jan 2016 Michael Waechter, Mate Beljan, Simon Fuhrmann, Nils Moehrle, Johannes Kopf, Michael Goesele

Furthermore, using only geometric accuracy by itself does not allow evaluating systems that either lack a geometric scene representation or utilize coarse proxy geometry.

3D Reconstruction

Unsupervised Joint Object Discovery and Segmentation in Internet Images

no code implementations CVPR 2013 Michael Rubinstein, Armand Joulin, Johannes Kopf, Ce Liu

In contrast to previous co-segmentation methods, our algorithm performs well even in the presence of significant amounts of noise images (images not containing a common object), as typical for datasets collected from Internet search.

Object Discovery

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