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IEEE International Conference on robotics and Automation (ICRA)

作者： Seong-Heum Kim Gyeongmin Choe Byungtae Ahn In So Kweon Robotics and Computer Vision Laboratory School of Electrical Engineering KAIST Daejeon Republic of Korea

In this paper, we present a visual learning framework to retrieve a 3D model and estimate its pose from a single image. To increase the quantity and quality of training data, we define our simulation space in the near infrared (NIR) band, and utilize the quasi-Monte Carlo (MC) method for scalable photorealistic rendering of manufactured components. Two types of convolutional neural network (CNN) architectures are trained over these synthetic data and a relatively small amount of real data. The first CNN model seeks the most discriminative information and uses it to classify industrial components with fine-grained shape attributes. Once a 3D model is identified, one of the category-specific CNNs is tested for pose regression in the second phase. The mixed data for learning object categories is useful in domain adaptation and attention mechanism in our system. We validate our data-driven method with 88 component models, and the experimental results are qualitatively demonstrated. Also, the CNNs trained with various conditions of mixed data are quantitatively analyzed.

关键词： Three-dimensional displays Rendering (computer graphics) Computational modeling Solid modeling Shape Cameras Adaptation models

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Why is the Winner the Best?

Why is the Winner the Best?

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Conference on computer vision and Pattern Recognition (CVPR)

作者： M. Eisenmann A. Reinke V. Weru M. D. Tizabi F. Isensee T. J. Adler S. Ali V. Andrearczyk M. Aubreville U. Baid S. Bakas N. Balu S. Bano J. Bernal S. Bodenstedt A. Casella V. Cheplygina M. Daum M. De Bruijne A. Depeursinge R. Dorent J. Egger D. G. Ellis S. Engelhardt M. Ganz N. Ghatwary G. Girard P. Godau A. Gupta L. Hansen K. Harada M. Heinrich N. Heller A. Hering A. Huaulmé P. Jannin A. E. Kavur O. Kodym M. Kozubek J. Li H. Li J. Ma C. Martín-Isla B. Menze A. Noble V. Oreiller N. Padoy S. Pati K. Payette T. Rädsch J. Rafael-Patiño V. Singh Bawa S. Speidel C. H. Sudre K. Van Wijnen M. Wagner D. Wei A. Yamlahi M. H. Yap C. Yuan M. Zenk A. Zia D. Zimmerer D. Aydogan B. Bhattarai L. Bloch R. Brüngel J. Cho C. Choi Q. Dou I. Ezhov C. M. Friedrich C. Fuller R. R. Gaire A. Galdran Á. García Faura M. Grammatikopoulou S. Hong M. Jahanifar I. Jang A. Kadkhodamohammadi I. Kang F. Kofler S. Kondo H. Kuijf M. Li M. Luu T. Martinčič P. Morais M. A. Naser B. Oliveira D. Owen S. Pang J. Park S. Park S. Płotka E. Puybareau N. Rajpoot K. Ryu N. Saeed A. Shephard P. Shi D. Štepec R. Subedi G. Tochon H. R. Torres H. Urien J. L. Vilaça K. A. Wahid H. Wang J. Wang L. Wang X. Wang B. Wiestler M. Wodzinski F. Xia J. Xie Z. Xiong S. Yang Y. Yang Z. Zhao K. Maier-Hein P. F. Jäger A. Kopp-Schneider L. Maier-Hein Division of Intelligent Medical Systems German Cancer Research Center (DKFZ) Heidelberg Germany Helmholtz Imaging German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Division of Biostatistics German Cancer Research Center (DKFZ) Heidelberg Germany Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Engineering and Physical Sciences School of Computing University of Leeds Leeds UK Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Sierre Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Lausanne Switzerland Technische Hochschule Ingolstadt Ingolstadt Germany Center for Artificial Intelligence and Data Science for Integrated Diagnostics (AI2D) and Center for Biomedical Image Computing and Analytics (CBICA) University of Pennsylvania Philadelphia PA USA Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology University of Washington Seattle WA USA Department of Computer Science Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) University College London London UK Universitat Autònoma de Barcelona & Computer Vision Center Barcelona Spain Division of Translational Surgical Oncology National Center for Tumor Diseases (NCT/UCC) Dresden Dresden Germany Department of Advanced Robotics Istituto Italiano di Tecnologia Italy Department of Electronics Information and Bioengineering Politecnico di Milano Milan Italy IT University of Copenhagen Copenhagen Denmark Department of General Visceral and Transplantation Surgery Heidelberg University Hospital Heidelberg Germany Department of Radiology and Nuc

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work.

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LINE MEETS AS-PROJECTIVE-AS-POSSIBLE IMAGE STITCHING WITH MOVING DLT

LINE MEETS AS-PROJECTIVE-AS-POSSIBLE IMAGE STITCHING WITH MO...

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IEEE International Conference on Image Processing

作者： Kyungdon Joo Namil Kim Tae-Hyun Oh In So Kweon Robotics and Computer Vision Laboratory KAIST

ISBN: (纸本)9781479983407

We propose a spatially varying stitching method with line correspondences. We are motivated by the observation that point features could be spatially biased or not matched in practice, e.g., repeated textures or homogeneous regions of man-made structures. In this scenario, line matches can provide strong correspondences as well as supplement cues, such as the structure preserving property. With these advantages, we adopt a feature fusion method that combines point and line correspondences into a unified framework for spatially varying stitching. We then estimate the balancing parameter between the point and line terms using geometric error. Our experiments show accurate alignment for challenging but common cases.

关键词： Spatially varying stitching Image alignment Line correspondence Moving direct linear transformation image registration fusion method sewing isostructure geometrical error images matching line

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vision System and Depth Processing for DRC-HUBO+

Vision System and Depth Processing for DRC-HUBO+

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IEEE International Conference on robotics and Automation

作者： Inwook Shim Seunghak Shin Yunsu Bok Kyungdon Joo Dong-Geol Choi Joon-Young Lee Jaesik Park Jun-Ho Oh In So Kweon Robotics and Computer Vision Laboratory Dep. of EE KAIST Daejeon 305-701 Korea Adobe Research San Jose CA 95110 United States Intel Labs Santa Clara CA 95054 United States Humanoid Research Center Dep. of ME KAIST Daejeon 305-701 Korea

ISBN: (纸本)9781467380270

This paper presents a vision system and a depth processing algorithm for DRC-HUBO+, the winner of the DRC finals 2015. Our system is designed to reliably capture 3D information of a scene and objects and to be robust to challenging environment conditions. We also propose a depth-map upsampling method that produces an outliers-free depth map by explicitly handling depth outliers. Our system is suitable for robotic applications in which a robot interacts with the real-world, requiring accurate object detection and pose estimation. We evaluate our depth processing algorithm in comparison with state-of-the-art algorithms on several synthetic and real-world datasets.

关键词： System Depth Processing

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Cost-aware depth map estimation for Lytro camera

Cost-aware depth map estimation for Lytro camera

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作者： Kim, Min-Jung Oh, Tae-Hyun Kweon, In So Robotics and Computer Vision Laboratory KAIST Korea Republic of

ISBN: (纸本)9781479957514

Since commercial light field cameras became available, the light field camera has aroused much interest from computer vision and image processing communities due to its versatile functions. Most of its special features are based on an estimated depth map, so reliable depth estimation is a crucial step. However, estimating depth on real light field cameras is a challenging problem due to noise and short baselines among sub-aperture images. We propose a depth map estimation method for light field cameras by exploiting correspondence and focus cues. We aggregate costs among all the sub-aperture images on cost volume to alleviate noise effects. With efficiency of the cost volume, cost-aware depth estimation is quickly achieved by discrete-continuous optimization. In addition, we analyze each property of correspondence and focus cues and utilize them to select reliable anchor points. A well reconstructed initial depth map from the anchors is shown to enhance convergence. We show our method outperforms the state-of-the-art methods by validating it on real datasets acquired with a Lytro camera. © 2014 IEEE.

关键词： Cameras

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Cost-aware depth map estimation for Lytro camera

Cost-aware depth map estimation for Lytro camera

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IEEE International Conference on Image Processing

作者： Min-Jung Kim Tae-Hyun Oh In So Kweon Robotics and Computer Vision Laboratory KAIST

ISBN: (纸本)9781479957521

关键词： Cameras Estimation Reliability Optimization Three-dimensional displays computer vision Conferences

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Auto-adjusting Camera Exposure for Outdoor robotics using Gradient Information

Auto-adjusting Camera Exposure for Outdoor Robotics using Gr...

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IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Inwook Shim Joon-Young Lee In So Kweon Robotics and Computer Vision Laboratory Division of Future Vehicle KAIST Daejeon 305-701 Korea Robotics and Computer Vision Laboratory Department of Electrical Engineering KAIST Daejeon 305-701 Korea Department of Electrical Engineering KAIST Daejeon 307-701 Korea

ISBN: (纸本)9781479969357

We present a new method to auto-adjust camera exposure for outdoor robotics. In outdoor environments, scene dynamic range may be wider than the dynamic range of the cameras due to sunlight and skylight. This can results in failures of vision-based algorithms because important image features are missing due to under-/over-saturation. To solve the problem, we adjust camera exposure to maximize image features in the gradient domain. By exploiting the gradient domain, our method naturally determines the proper exposure needed to capture important image features in a manner that is robust against illumination conditions. The proposed method is implemented using an off-the-shelf machine vision camera and is evaluated using outdoor robotics applications. Experimental results demonstrate the effectiveness of our method, which improves the performance of robot vision algorithms.

关键词： Cameras Lighting Robot vision systems Manuals Dynamic range Heuristic algorithms

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RGBD sensor and mirrors: A practical setup for 3D reconstruction of dynamic objects

RGBD sensor and mirrors: A practical setup for 3D reconstruc...

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International Conference on Ubiquitous Robots and Ambient Intelligence (URAI)

作者： Min-Hyun Kim Jaesik Park In So Kweon Robotics and Computer Vision Laboratory KAIST Daejeon Korea Korea Advanced Institute of Science and Technology Daejeon Daejeon KR

ISBN: (纸本)9781479953349

In order to get full 3D model from consumer depth cameras, previous approaches used set of synchronized depth cameras or moved the depth sensor around the target object. This on-going work introduces a cost effective and convenient framework for filming dynamic 3D object. This set is comprised of a consumer depth camera, or RGBD sensor and a pair of mirrors. Because of the images reflected from two mirrors in the system, it can capture a depth-map of the target object in an extended view. The practicality and effectiveness of this framework is demonstrated.

关键词： Three-dimensional displays Mirrors Robot sensing systems Cameras Mathematical model Equations Joints

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Control Strategies for a Humanoid Robot to Drive and then Egress a Utility Vehicle for Remote Approach

Control Strategies for a Humanoid Robot to Drive and then Eg...

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IEEE-RAS International Conference on Humanoid Robots

作者： Hyobin Jeong Jaesung Oh Mingeuk Kim Kyungdon Joo In So Kweon Jun-Ho Oh Humanoid Research Center School of Mechanical Aerospace & Systems Engineering Department of Mechanical Engineering Korea Advanced Institute of Science and Technology Robotics and Computer Vision Laboratory School of Mechanical Aerospace & Systems Engineering Department of Electrical Engineering Korea Advanced Institute of Science and Technology Department of Electrical Engineering KAIST

ISBN: (纸本)9781479968862

This paper proposes strategies for the driving and egress of a vehicle with a humanoid robot. To drive the vehicle, the RANSAC method was used to detect obstacles, and the Wagon model was used to control the steering and velocity of the vehicle with only a limited number of sensors which were installed on the humanoid robot. Additionally, a manual teleoperating method was used with the lane projection technique. For the egress motion, gain override and the Cartesian position/force control technique were used to interact with the vehicle structure. To overcome the disadvantages of a highly geared manipulator, a special technique was used that included modelled friction compensation and a non-complementary switching mode. DRC-HUBO+ used the proposed method to perform a vehicle driving and egress task in the DRC finals 2015.

关键词： Humanoid robots EGRESS vehicle Vehicle driving Automobile driving UTILITY VEHICLES control strategy friction compensation DRIVES Steering

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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

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