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Wei An - IEEE Xplore Author Profile

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Author details

Wei An

Publications

91

Citations

3,245

Publications by Year

20122025

Co-Authors:

Chengjin AnYaoming CaiFanzhi CaoDong CaoXu Cao

Show All Co-Authors (88)

Wei An

Affiliation

College of Electronic Science and Technology, National University of Defense Technology (NUDT), P. R. China

Publication Topics

Detection Performance,
False Alarm Rate,
Feature Maps,
Convolutional Neural Network,
Light Field,
Background Clutter,
Convolutional Layers,
Deeper Layers,
False Alarm,
Object Detection,
Spatial Information,
Super-resolution

Biography

Wei An received the Ph.D. degree from the National University of Defense Technology (NUDT), Changsha, China, in 1999.,In 2016, she visited the University of Southampton, Southampton, U.K., as a Senior Visiting Scholar. She is currently a Professor with the College of Electronic Science and Technology, NUDT. She has authored or coauthored over 100 journal and conference publications. Her research interests include signal processing and image processing.(Based on document published on 18 January 2022).

Publications

91

Citations

3,245

Publications by Year

20122025

Co-Authors:

Chengjin An
Yaoming Cai
Fanzhi Cao
Dong Cao
Xu Cao

Show All Co-Authors (88)

Author's Published Works

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Showing 1-20 of 20 results

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Christopher Schroers(20)
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Disney Research(4)
ETH Zürich(4)
Department of Computer Science, ETH Zurich(3)
DisneyResearch|Studios(3)
Disney Research | Studios(2)
2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)(3)
2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)(3)
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition(2)
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)(2)
2016 Fourth International Conference on 3D Vision (3DV)(1)
IEEE(20)
Media(3)
Salt Lake City, UT, USA(4)
Seattle, WA, USA(3)
Vancouver, BC, Canada(3)
Waikoloa, HI, USA(2)
Davos, Switzerland(1)
Optical Flow(7)
Input Image(6)
Neural Network(6)
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Results

Efficient Video Encoder Autotuning via Offline Bayesian Optimization and Supervised Learning

Roberto Azevedo;Yuanyi Xue;Xuewei Meng;Wenhao Zhang;Scott Labrozzi;Christopher Schroers

2024 IEEE 26th International Workshop on Multimedia Signal Processing (MMSP)

Year: 2024 | Conference Paper |

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Modern video encoders are complex software containing dozens of parameters, which allows them to be configured to different scenarios, requirements, or specific titles or scenes. Besides the number of parameters, the inter-dependency between them adds to the complexity of finding a per-title optimized combination of encoding parameters. Even though good practices in the industry have emerged, with...Show More

Efficient Video Encoder Autotuning via Offline Bayesian Optimization and Supervised Learning

Roberto Azevedo;Yuanyi Xue;Xuewei Meng;Wenhao Zhang;Scott Labrozzi;Christopher Schroers

2024 IEEE 26th International Workshop on Multimedia Signal Processing (MMSP)

Year: 2024 | Conference Paper |

DiVAS: Video and Audio Synchronization with Dynamic Frame Rates

Clara Fernandez-Labrador;Mertcan Akçay;Eitan Abecassis;Joan Massich;Christopher Schroers

2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2024 | Conference Paper |

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Synchronization issues between audio and video are one of the most disturbing quality defects in film production and live broadcasting. Even a discrepancy as short as 45 milliseconds can degrade the viewer's experience enough to warrant manual quality checks over entire movies. In this paper, we study the automatic discovery of such issues. Specifically, we focus on the alignment of lip movements ...Show More

DiVAS: Video and Audio Synchronization with Dynamic Frame Rates

Clara Fernandez-Labrador;Mertcan Akçay;Eitan Abecassis;Joan Massich;Christopher Schroers

2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2024 | Conference Paper |

QUADify: Extracting Meshes with Pixel-Level Details and Materials from Images

Maximilian Frühauf;Hayko Riemenschneider;Markus Gross;Christopher Schroers

2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2024 | Conference Paper |

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Despite exciting progress in automatic 3D reconstruction from images, excessive and irregular triangular faces in the resulting meshes still constitute a significant challenge when it comes to adoption in practical artist work-flows. Therefore, we propose a method to extract regular quad-dominant meshes from posed images. More specifically, we generate a high-quality 3D model through de-compositio...Show More

QUADify: Extracting Meshes with Pixel-Level Details and Materials from Images

Maximilian Frühauf;Hayko Riemenschneider;Markus Gross;Christopher Schroers

2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2024 | Conference Paper |

Combining Frame and GOP Embeddings for Neural Video Representation

Jens Eirik Saethre;Roberto Azevedo;Christopher Schroers

2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2024 | Conference Paper |

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Implicit neural representations (INRs) were recently proposed as a new video compression paradigm, with existing approaches performing on par with HEVC. However, such methods only perform well in limited settings, e.g., specific model sizes, fixed aspect ratios, and low-motion videos. We address this issue by proposing T-NeRV, a hybrid video INR that combines framespecific embeddings with GOP-spec...Show More

Combining Frame and GOP Embeddings for Neural Video Representation

Jens Eirik Saethre;Roberto Azevedo;Christopher Schroers

2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2024 | Conference Paper |

CoARF: Controllable 3D Artistic Style Transfer for Radiance Fields

Deheng Zhang;Clara Fernandez-Labrador;Christopher Schroers

2024 International Conference on 3D Vision (3DV)

Year: 2024 | Conference Paper |

Cited by: Papers (2)

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Creating artistic 3D scenes can be time-consuming and requires specialized knowledge. To address this, recent works such as ARF [57], use a radiance field-based approach with style constraints to generate 3D scenes that resemble a style image provided by the user. However, these methods lack fine-grained control over the resulting scenes. In this paper, we introduce Controllable Artistic Radiance ...Show More

CoARF: Controllable 3D Artistic Style Transfer for Radiance Fields

Deheng Zhang;Clara Fernandez-Labrador;Christopher Schroers

2024 International Conference on 3D Vision (3DV)

Year: 2024 | Conference Paper |

Stereo Conversion with Disparity-Aware Warping, Compositing and Inpainting

Lukas Mehl;Andrés Bruhn;Markus Gross;Christopher Schroers

2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)

Year: 2024 | Conference Paper |

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Despite of exciting advances in image-based rendering and novel view synthesis, it is still challenging to achieve high-resolution results that can reach production-level quality when applying such methods to the task of stereo conversion. At the same time, only very few dedicated stereo conversion approaches exist, which also fall short in terms of the required quality. Hence, in this paper, we p...Show More

Stereo Conversion with Disparity-Aware Warping, Compositing and Inpainting

Lukas Mehl;Andrés Bruhn;Markus Gross;Christopher Schroers

2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)

Year: 2024 | Conference Paper |

Controllable Inversion of Black-Box Face Recognition Models via Diffusion

Manuel Kansy;Anton Raël;Graziana Mignone;Jacek Naruniec;Christopher Schroers;Markus Gross;Romann M. Weber

2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)

Year: 2023 | Conference Paper |

Cited by: Papers (7)

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Face recognition models embed a face image into a low-dimensional identity vector containing abstract encodings of identity-specific facial features that allow individuals to be distinguished from one another. We tackle the challenging task of inverting the latent space of pre-trained face recognition models without full model access (i.e. black-box setting). A variety of methods have been propose...Show More

Controllable Inversion of Black-Box Face Recognition Models via Diffusion

Manuel Kansy;Anton Raël;Graziana Mignone;Jacek Naruniec;Christopher Schroers;Markus Gross;Romann M. Weber

2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)

Year: 2023 | Conference Paper |

Frame Interpolation Transformer and Uncertainty Guidance

Markus Plack;Matthias B. Hullin;Karlis Martins Briedis;Markus Gross;Abdelaziz Djelouah;Christopher Schroers

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2023 | Conference Paper |

Cited by: Papers (2)

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Video frame interpolation has seen important progress in recent years, thanks to developments in several directions. Some works leverage better optical flow methods with improved splatting strategies or additional cues from depth, while others have investigated alternative approaches through direct predictions or transformers. Still, the problem remains unsolved in more challenging conditions such...Show More

Frame Interpolation Transformer and Uncertainty Guidance

Markus Plack;Matthias B. Hullin;Karlis Martins Briedis;Markus Gross;Abdelaziz Djelouah;Christopher Schroers

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2023 | Conference Paper |

Kernel Aware Resampler

Michael Bernasconi;Abdelaziz Djelouah;Farnood Salehi;Markus Gross;Christopher Schroers

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2023 | Conference Paper |

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Deep learning based methods for super-resolution have become state-of-the-art and outperform traditional approaches by a significant margin. From the initial models designed for fixed integer scaling factors (e.g. $\times 2$ or $\times 4)$), efforts were made to explore different directions such as modeling blur kernels or addressing non-integer scaling factors. However, existing works do not prov...Show More

Kernel Aware Resampler

Michael Bernasconi;Abdelaziz Djelouah;Farnood Salehi;Markus Gross;Christopher Schroers

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2023 | Conference Paper |

Video Compression with Entropy-Constrained Neural Representations

Carlos Gomes;Roberto Azevedo;Christopher Schroers

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2023 | Conference Paper |

Cited by: Papers (9)

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Encoding videos as neural networks is a recently proposed approach that allows new forms of video processing. However, traditional techniques still outperform such neural video representation (NVR) methods for the task of video compression. This performance gap can be explained by the fact that current NVR methods: i) use architectures that do not efficiently obtain a compact representation of tem...Show More

Video Compression with Entropy-Constrained Neural Representations

Carlos Gomes;Roberto Azevedo;Christopher Schroers

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2023 | Conference Paper |

Large-Scale Multi-Site Subjective Assessment on Image Banding Artifacts

Yuanyi Xue;Roberto Azevedo;Xuchang Huangfu;Yang Zhang;Christopher Schroers;Scott Labrozzi

2023 15th International Conference on Quality of Multimedia Experience (QoMEX)

Year: 2023 | Conference Paper |

Cited by: Papers (2)

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Banding largely occurs due to the finite bit depth representation of digital media and displays. It is a unique type of artifact that is both challenging to accurately measure and difficult to properly mitigate. These artifacts can also be observed across a wide spectrum of streaming services, no matter if professional studio productions or user-generated content. Inspired by the need to monitor a...Show More

Large-Scale Multi-Site Subjective Assessment on Image Banding Artifacts

Yuanyi Xue;Roberto Azevedo;Xuchang Huangfu;Yang Zhang;Christopher Schroers;Scott Labrozzi

2023 15th International Conference on Quality of Multimedia Experience (QoMEX)

Year: 2023 | Conference Paper |

Self-Supervised Effective Resolution Estimation with Adversarial Augmentations

Manuel Kansy;Julian Balletshofer;Jacek Naruniec;Christopher Schroers;Graziana Mignone;Markus Gross;Romann M. Weber

2023 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW)

Year: 2023 | Conference Paper |

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High-resolution, high-quality images of human faces are desired as training data and output for many modern applications, such as avatar generation, face super-resolution, and face swapping. The terms high-resolution and high-quality are often used interchangeably; however, the two concepts are not equivalent, and high-resolution does not always imply high-quality. To address this, we motivate and...Show More

Self-Supervised Effective Resolution Estimation with Adversarial Augmentations

Manuel Kansy;Julian Balletshofer;Jacek Naruniec;Christopher Schroers;Graziana Mignone;Markus Gross;Romann M. Weber

2023 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW)

Year: 2023 | Conference Paper |

Generic Image Restoration with Flow Based Priors

Leonhard Helminger;Michael Bernasconi;Abdelaziz Djelouah;Markus Gross;Christopher Schroers

2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

Year: 2021 | Conference Paper |

Cited by: Papers (2)

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Image restoration has seen great progress in the last years thanks to the advances in deep neural networks. Most of these existing techniques are trained using full supervision with suitable image pairs to tackle a specific degradation. However, in a generic setting with unknown degradations this is not possible and a good prior remains crucial. Recently, neural network based approaches have been ...Show More

Generic Image Restoration with Flow Based Priors

Leonhard Helminger;Michael Bernasconi;Abdelaziz Djelouah;Markus Gross;Christopher Schroers

2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

Year: 2021 | Conference Paper |

Neural Inter-Frame Compression for Video Coding

Abdelaziz Djelouah;Joaquim Campos;Simone Schaub-Meyer;Christopher Schroers

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

Year: 2019 | Conference Paper |

Cited by: Papers (108)

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While there are many deep learning based approaches for single image compression, the field of end-to-end learned video coding has remained much less explored. Therefore, in this work we present an inter-frame compression approach for neural video coding that can seamlessly build up on different existing neural image codecs. Our end-to-end solution performs temporal prediction by optical flow base...Show More

Neural Inter-Frame Compression for Video Coding

Abdelaziz Djelouah;Joaquim Campos;Simone Schaub-Meyer;Christopher Schroers

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

Year: 2019 | Conference Paper |

Light Field Synthesis Using Inexpensive Surveillance Camera Systems

Frederike Dümbgen;Christopher Schroers;Kenny Mitchell

2019 IEEE International Conference on Image Processing (ICIP)

Year: 2019 | Conference Paper |

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We present a light field synthesis technique that achieves accurate reconstruction given a low-cost, wide-baseline camera rig. Our system integrates optical flow with methods for rectification, disparity estimation, and feature extraction, which we then feed to a neural network view synthesis solver with wide-baseline capability. We propose two novel warping methods that improve the accuracy of di...Show More

Light Field Synthesis Using Inexpensive Surveillance Camera Systems

Frederike Dümbgen;Christopher Schroers;Kenny Mitchell

2019 IEEE International Conference on Image Processing (ICIP)

Year: 2019 | 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)

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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

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

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)

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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

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

Year: 2018 | Conference Paper |

PhaseNet for Video Frame Interpolation

Simone Meyer;Abdelaziz Djelouah;Brian McWilliams;Alexander Sorkine-Hornung;Markus Gross;Christopher Schroers

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition

Year: 2018 | Conference Paper |

Cited by: Papers (132)

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Most approaches for video frame interpolation require accurate dense correspondences to synthesize an in-between frame. Therefore, they do not perform well in challenging scenarios with e.g. lighting changes or motion blur. Recent deep learning approaches that rely on kernels to represent motion can only alleviate these problems to some extent. In those cases, methods that use a per-pixel phase-ba...Show More

PhaseNet for Video Frame Interpolation

Simone Meyer;Abdelaziz Djelouah;Brian McWilliams;Alexander Sorkine-Hornung;Markus Gross;Christopher Schroers

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition

Year: 2018 | Conference Paper |

Normalized Cut Loss for Weakly-Supervised CNN Segmentation

Meng Tang;Abdelaziz Djelouah;Federico Perazzi;Yuri Boykov;Christopher Schroers

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition

Year: 2018 | Conference Paper |

Cited by: Papers (230)

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Most recent semantic segmentation methods train deep convolutional neural networks with fully annotated masks requiring pixel-accuracy for good quality training. Common weakly-supervised approaches generate full masks from partial input (e.g. scribbles or seeds) using standard interactive segmentation methods as preprocessing. But, errors in such masks result in poorer training since standard loss...Show More

Normalized Cut Loss for Weakly-Supervised CNN Segmentation

Meng Tang;Abdelaziz Djelouah;Federico Perazzi;Yuri Boykov;Christopher Schroers

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition

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 |

Cited by: Papers (59) | Patents (7)

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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

2016 Fourth International Conference on 3D Vision (3DV)

Year: 2016 | Conference Paper |

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