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Olga Sorkine-Hornung - IEEE Xplore Author Profile
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Olga Sorkine-Hornung

Publications
13
Citations
765
Publications by Year
20142023
Co-Authors:
Show All Co-Authors (133)
Image of author Olga Sorkine-Hornung

Olga Sorkine-Hornung

Affiliation

ETH Zurich

Publication Topics

Biography

Olga Sorkine-Hornung received the BSc degree in mathematics and computer science and the PhD degree in computer science from Tel Aviv University in 2000 and 2006, respectively. From 2008 to 2011, she was an assistant professor with the Courant Institute of Mathematical Sciences, New York University. She is currently a professor of computer science with ETH Zurich. She currently leads the Interactive Geometry Lab, Institute of Visual Computing. Her research interests include geometry processing and interactive geometric modeling.(Based on document published on 30 November 2021).
Publications
13
Citations
765
Publications by Year
20142023
Co-Authors:
Show All Co-Authors (133)
Author's Published Works

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MoDi: Unconditional Motion Synthesis from Diverse Data

Sigal Raab;Inbal Leibovitch;Peizhuo Li;Kfir Aberman;Olga Sorkine-Hornung;Daniel Cohen-Or

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2023 | Conference Paper |
Cited by: Papers (17)
The emergence of neural networks has revolutionized the field of motion synthesis. Yet, learning to unconditionally synthesize motions from a given distribution remains challenging, especially when the motions are highly diverse. In this work, we present MoDi - a generative model trained in an unsupervised setting from an extremely diverse, unstructured and unlabeled dataset. During inference, MoD...Show More

MoDi: Unconditional Motion Synthesis from Diverse Data

Sigal Raab;Inbal Leibovitch;Peizhuo Li;Kfir Aberman;Olga Sorkine-Hornung;Daniel Cohen-Or

Year: 2023 | Conference Paper |

Self-Calibrated Multi-Sensor Wearable for Hand Tracking and Modeling

Nikhil Gosala;Fangjinhua Wang;Zhaopeng Cui;Hanxue Liang;Oliver Glauser;Shihao Wu;Olga Sorkine-Hornung

IEEE Transactions on Visualization and Computer Graphics
Year: 2023 | Volume: 29, Issue: 3 | Journal Article |
Cited by: Papers (8)
We present a multi-sensor system for consistent 3D hand pose tracking and modeling that leverages the advantages of both wearable and optical sensors. Specifically, we employ a stretch-sensing soft glove and three IMUs in combination with an RGB-D camera. Different sensor modalities are fused based on the availability and confidence estimation, enabling seamless hand tracking in challenging enviro...Show More

Self-Calibrated Multi-Sensor Wearable for Hand Tracking and Modeling

Nikhil Gosala;Fangjinhua Wang;Zhaopeng Cui;Hanxue Liang;Oliver Glauser;Shihao Wu;Olga Sorkine-Hornung

Year: 2023 | Volume: 29, Issue: 3 | Journal Article |

Smooth Non-Rigid Shape Matching via Effective Dirichlet Energy Optimization

Robin Magnet;Jing Ren;Olga Sorkine-Hornung;Maks Ovsjanikov

2022 International Conference on 3D Vision (3DV)
Year: 2022 | Conference Paper |
Cited by: Papers (15)
We introduce pointwise map smoothness via the Dirich-let energy into the functional map pipeline, and propose an algorithm for optimizing it efficiently, which leads to highquality results in challenging settings. Specifically, we first formulate the Dirichlet energy of the pulled-back shape coordinates, as a way to evaluate smoothness of a pointwise map across discrete surfaces. We then extend th...Show More

Smooth Non-Rigid Shape Matching via Effective Dirichlet Energy Optimization

Robin Magnet;Jing Ren;Olga Sorkine-Hornung;Maks Ovsjanikov

Year: 2022 | Conference Paper |

Iso-Points: Optimizing Neural Implicit Surfaces with Hybrid Representations

Wang Yifan;Shihao Wu;Cengiz Öztireli;Olga Sorkine-Hornung

2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2021 | Conference Paper |
Cited by: Papers (24)
Neural implicit functions have emerged as a powerful representation for surfaces in 3D. Such a function can en-code a high quality surface with intricate details into the parameters of a deep neural network. However, optimizing for the parameters for accurate and robust reconstructions remains a challenge, especially when the input data is noisy or incomplete. In this work, we develop a hybrid neu...Show More

Iso-Points: Optimizing Neural Implicit Surfaces with Hybrid Representations

Wang Yifan;Shihao Wu;Cengiz Öztireli;Olga Sorkine-Hornung

Year: 2021 | Conference Paper |

Neural Cages for Detail-Preserving 3D Deformations

Wang Yifan;Noam Aigerman;Vladimir G. Kim;Siddhartha Chaudhuri;Olga Sorkine-Hornung

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2020 | Conference Paper |
Cited by: Papers (65)
We propose a novel learnable representation for detail preserving shape deformation. The goal of our method is to warp a source shape to match the general structure of a target shape, while preserving the surface details of the source. Our method extends a traditional cage-based deformation technique, where the source shape is enclosed by a coarse control mesh termed cage, and translations prescri...Show More

Neural Cages for Detail-Preserving 3D Deformations

Wang Yifan;Noam Aigerman;Vladimir G. Kim;Siddhartha Chaudhuri;Olga Sorkine-Hornung

Year: 2020 | Conference Paper |

Patch-Based Progressive 3D Point Set Upsampling

Wang Yifan;Shihao Wu;Hui Huang;Daniel Cohen-Or;Olga Sorkine-Hornung

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2019 | Conference Paper |
Cited by: Papers (169)
We present a detail-driven deep neural network for point set upsampling. A high-resolution point set is essential for point-based rendering and surface reconstruction. Inspired by the recent success of neural image super-resolution techniques, we progressively train a cascade of patch-based upsampling networks on different levels of detail end-to-end. We propose a series of architectural design co...Show More

Patch-Based Progressive 3D Point Set Upsampling

Wang Yifan;Shihao Wu;Hui Huang;Daniel Cohen-Or;Olga Sorkine-Hornung

Year: 2019 | Conference Paper |

Deformation Capture via Self-Sensing Capacitive Arrays (Video)

Oliver Glauser;Daniele Panozzo;Otmar Hilliges;Olga Sorkine-Hornung

2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Year: 2018 | Conference Paper |
Cited by: Papers (1)
In this video we present soft self-sensing capacitive arrays and demonstrate their use in capturing dense surface deformations without requiring line of sight. The capacitive arrays are made of two electrode patterns embedded into a single silicone compound. The overlaps of the electrode strip patterns form local capacitors. As the sensor is stretched the local capacitance measurements change. We ...Show More

Deformation Capture via Self-Sensing Capacitive Arrays (Video)

Oliver Glauser;Daniele Panozzo;Otmar Hilliges;Olga Sorkine-Hornung

Year: 2018 | Conference Paper |

A Fully Progressive Approach to Single-Image Super-Resolution

Yifan Wang;Federico Perazzi;Brian McWilliams;Alexander Sorkine-Hornung;Olga Sorkine-Hornung;Christopher Schroers

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Year: 2018 | Conference Paper |
Cited by: Papers (178)
Recent deep learning approaches to single image superresolution have achieved impressive results in terms of traditional error measures and perceptual quality. However, in each case it remains challenging to achieve high quality results for large upsampling factors. To this end, we propose a method (ProSR) that is progressive both in architecture and training: the network upsamples an image in int...Show More

A Fully Progressive Approach to Single-Image Super-Resolution

Yifan Wang;Federico Perazzi;Brian McWilliams;Alexander Sorkine-Hornung;Olga Sorkine-Hornung;Christopher Schroers

Year: 2018 | Conference Paper |

NTIRE 2018 Challenge on Single Image Super-Resolution: Methods and Results

Radu Timofte;Shuhang Gu;Jiqing Wu;Luc Van Gool;Lei Zhang;Ming-Hsuan Yang;Muhammad Haris;Greg Shakhnarovich;Norimichi Ukita;Shijia Hu;Yijie Bei;Zheng Hui;Xiao Jiang;Yanan Gu;Jie Liu;Yifan Wang;Federico Perazzi;Brian McWilliams;Alexander Sorkine-Hornung;Olga Sorkine-Hornung;Christopher Schroers;Jiahui Yu;Yuchen Fan;Jianchao Yang;Ning Xu;Zhaowen Wang;Xinchao Wang;Thomas S. Huang;Xintao Wang;Ke Yu;Tak-Wai Hui;Chao Dong;Liang Lin;Chen Change Loy;Dongwon Park;Kwanyoung Kim;Se Young Chun;Kai Zhang;Pengjv Liu;Wangmeng Zuo;Shi Guo;Jiye Liu;Jinchang Xu;Yijiao Liu;Fengye Xiong;Yuan Dong;Hongliang Bai;Alexandru Damian;Nikhil Ravi;Sachit Menon;Cynthia Rudin;Junghoon Seo;Taegyun Jeon;Jamyoung Koo;Seunghyun Jeon;Soo Ye Kim;Jae-Seok Choi;Sehwan Ki;Soomin Seo;Hyeonjun Sim;Saehun Kim;Munchurl Kim;Rong Chen;Kun Zeng;Jinkang Guo;Yanyun Qu;Cuihua Li;Namhyuk Ahn;Byungkon Kang;Kyung-Ah Sohn;Yuan Yuan;Jiawei Zhang;Jiahao Pang;Xiangyu Xu;Yan Zhao;Wei Deng;Sibt Ul Hussain;Muneeb Aadil;Rafia Rahim;Xiaowang Cai;Fang Huang;Yueshu Xu;Pablo Navarrete Michelini;Dan Zhu;Hanwen Liu;Jun-Hyuk Kim;Jong-Seok Lee;Yiwen Huang;Ming Qiu;Liting Jing;Jiehang Zeng;Ying Wang;Manoj Sharma;Rudrabha Mukhopadhyay;Avinash Upadhyay;Sriharsha Koundinya;Ankit Shukla;Santanu Chaudhury;Zhe Zhang;Yu Hen Hu;Lingzhi Fu

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Year: 2018 | Conference Paper |
Cited by: Papers (184)
This paper reviews the 2nd NTIRE challenge on single image super-resolution (restoration of rich details in a low resolution image) with focus on proposed solutions and results. The challenge had 4 tracks. Track 1 employed the standard bicubic downscaling setup, while Tracks 2, 3 and 4 had realistic unknown downgrading operators simulating camera image acquisition pipeline. The operators were lear...Show More

NTIRE 2018 Challenge on Single Image Super-Resolution: Methods and Results

Radu Timofte;Shuhang Gu;Jiqing Wu;Luc Van Gool;Lei Zhang;Ming-Hsuan Yang;Muhammad Haris;Greg Shakhnarovich;Norimichi Ukita;Shijia Hu;Yijie Bei;Zheng Hui;Xiao Jiang;Yanan Gu;Jie Liu;Yifan Wang;Federico Perazzi;Brian McWilliams;Alexander Sorkine-Hornung;Olga Sorkine-Hornung;Christopher Schroers;Jiahui Yu;Yuchen Fan;Jianchao Yang;Ning Xu;Zhaowen Wang;Xinchao Wang;Thomas S. Huang;Xintao Wang;Ke Yu;Tak-Wai Hui;Chao Dong;Liang Lin;Chen Change Loy;Dongwon Park;Kwanyoung Kim;Se Young Chun;Kai Zhang;Pengjv Liu;Wangmeng Zuo;Shi Guo;Jiye Liu;Jinchang Xu;Yijiao Liu;Fengye Xiong;Yuan Dong;Hongliang Bai;Alexandru Damian;Nikhil Ravi;Sachit Menon;Cynthia Rudin;Junghoon Seo;Taegyun Jeon;Jamyoung Koo;Seunghyun Jeon;Soo Ye Kim;Jae-Seok Choi;Sehwan Ki;Soomin Seo;Hyeonjun Sim;Saehun Kim;Munchurl Kim;Rong Chen;Kun Zeng;Jinkang Guo;Yanyun Qu;Cuihua Li;Namhyuk Ahn;Byungkon Kang;Kyung-Ah Sohn;Yuan Yuan;Jiawei Zhang;Jiahao Pang;Xiangyu Xu;Yan Zhao;Wei Deng;Sibt Ul Hussain;Muneeb Aadil;Rafia Rahim;Xiaowang Cai;Fang Huang;Yueshu Xu;Pablo Navarrete Michelini;Dan Zhu;Hanwen Liu;Jun-Hyuk Kim;Jong-Seok Lee;Yiwen Huang;Ming Qiu;Liting Jing;Jiehang Zeng;Ying Wang;Manoj Sharma;Rudrabha Mukhopadhyay;Avinash Upadhyay;Sriharsha Koundinya;Ankit Shukla;Santanu Chaudhury;Zhe Zhang;Yu Hen Hu;Lingzhi Fu

Year: 2018 | Conference Paper |

Point Cloud Noise and Outlier Removal for Image-Based 3D Reconstruction

Katja Wolff;Changil Kim;Henning Zimmer;Christopher Schroers;Mario Botsch;Olga Sorkine-Hornung;Alexander Sorkine-Hornung

2016 Fourth International Conference on 3D Vision (3DV)
Year: 2016 | Conference Paper |
Point sets generated by image-based 3D reconstruction techniques are often much noisier than those obtained using active techniques like laser scanning. Therefore, they pose greater challenges to the subsequent surface reconstruction (meshing) stage. We present a simple and effective method for removing noise and outliers from such point sets. Our algorithm uses the input images and corresponding ...Show More

Point Cloud Noise and Outlier Removal for Image-Based 3D Reconstruction

Katja Wolff;Changil Kim;Henning Zimmer;Christopher Schroers;Mario Botsch;Olga Sorkine-Hornung;Alexander Sorkine-Hornung

Year: 2016 | Conference Paper |

Depth from Gradients in Dense Light Fields for Object Reconstruction

Kaan Yucer;Changil Kim;Alexander Sorkine-Hornung;Olga Sorkine-Hornung

2016 Fourth International Conference on 3D Vision (3DV)
Year: 2016 | Conference Paper |
Objects with thin features and fine details are challenging for most multi-view stereo techniques, since such features occupy small volumes and are usually only visible in a small portion of the available views. In this paper, we present an efficient algorithm to reconstruct intricate objects using densely sampled light fields. At the heart of our technique lies a novel approach to compute per-pix...Show More

Depth from Gradients in Dense Light Fields for Object Reconstruction

Kaan Yucer;Changil Kim;Alexander Sorkine-Hornung;Olga Sorkine-Hornung

Year: 2016 | Conference Paper |

Reconstruction of Articulated Objects from a Moving Camera

Kaan Yücer;Oliver Wang;Alexander Sorkine-Hornung;Olga Sorkine-Hornung

2015 IEEE International Conference on Computer Vision Workshop (ICCVW)
Year: 2015 | Conference Paper |
Cited by: Papers (6)
Many scenes that we would like to reconstruct contain articulated objects, and are often captured by only a single, non-fixed camera. Existing techniques for reconstructing articulated objects either require templates, which can be challenging to acquire, or have difficulties with perspective effects and missing data. In this paper, we present a novel reconstruction pipeline that first treats each...Show More

Reconstruction of Articulated Objects from a Moving Camera

Kaan Yücer;Oliver Wang;Alexander Sorkine-Hornung;Olga Sorkine-Hornung

Year: 2015 | Conference Paper |

Fast and Memory-Efficienty Topological Denoising of 2D and 3D Scalar Fields

David Günther;Alec Jacobson;Jan Reininghaus;Hans-Peter Seidel;Olga Sorkine-Hornung;Tino Weinkauf

IEEE Transactions on Visualization and Computer Graphics
Year: 2014 | Volume: 20, Issue: 12 | Journal Article |
Cited by: Papers (23)
Data acquisition, numerical inaccuracies, and sampling often introduce noise in measurements and simulations. Removing this noise is often necessary for efficient analysis and visualization of this data, yet many denoising techniques change the minima and maxima of a scalar field. For example, the extrema can appear or disappear, spatially move, and change their value. This can lead to wrong inter...Show More

Fast and Memory-Efficienty Topological Denoising of 2D and 3D Scalar Fields

David Günther;Alec Jacobson;Jan Reininghaus;Hans-Peter Seidel;Olga Sorkine-Hornung;Tino Weinkauf

Year: 2014 | Volume: 20, Issue: 12 | Journal Article |

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