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检索条件"机构=KAIST Robotics and Computer Vision Laboratory"
30 条 记 录,以下是1-10 订阅
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Category-level metric scale object shape and pose estimation
arXiv
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arXiv 2021年
作者: Lee, Taeyeop Lee, Byeong-Uk Kim, Myungchul Kweon, In So Robotics and Computer Vision Laboratory KAIST Daejeon Korea Republic of
Advances in deep learning recognition have led to accurate object detection with 2D images. However, these 2D perception methods are insufficient for complete 3D world information. Concurrently, advanced 3D shape esti... 详细信息
来源: 评论
Correlate-and-excite: Real-time stereo matching via guided cost volume excitation
arXiv
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arXiv 2021年
作者: Bangunharcana, Antyanta Cho, Jae Won Lee, Seokju Kweon, In So Kim, Kyung-Soo Kim, Soohyun Mechatronics Systems and Control Laboratory KAIST Daejeon34141 Korea Republic of Robotics and Computer Vision Laboratory KAIST Daejeon34141 Korea Republic of
Volumetric deep learning approach towards stereo matching aggregates a cost volume computed from input left and right images using 3D convolutions. Recent works showed that utilization of extracted image features and ... 详细信息
来源: 评论
Understanding Adversarial Examples From the Mutual Influence of Images and Perturbations
Understanding Adversarial Examples From the Mutual Influence...
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Conference on computer vision and Pattern Recognition (CVPR)
作者: Chaoning Zhang Philipp Benz Tooba Imtiaz In So Kweon Robotics and Computer Vision (RCV) Laboratory Korea Advanced Institute of Science and Technology (KAIST) Daejeon Korea Korea Advanced Institute of Science and Technology Daejeon South Korea
A wide variety of works have explored the reason for the existence of adversarial examples, but there is no consensus on the explanation. We propose to treat the DNN logits as a vector for feature representation, and ... 详细信息
来源: 评论
Camera Exposure Control for Robust Robot vision with Noise-Aware Image Quality Assessment
Camera Exposure Control for Robust Robot Vision with Noise-A...
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2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
作者: Ukcheol Shin Jinsun Park Gyumin Shim Francois Rameau In So Kweon Robotics and Computer Vision Laboratory School of Electrical Engineering KAIST Daejeon Republic of Korea
In this paper, we propose a noise-aware exposure control algorithm for robust robot vision. Our method aims to capture best-exposed images, which can boost the performance of various computer vision and robotics tasks...
来源: 评论
Camera exposure control for robust robot vision with noise-aware image quality assessment
arXiv
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arXiv 2019年
作者: Shin, Ukcheol Park, Jinsun Shim, Gyumin Rameau, Francois Kweon, In So Robotics and Computer Vision Laboratory School of Electrical Engineering KAIST Daejeon34141 Korea Republic of
In this paper, we propose a noise-aware exposure control algorithm for robust robot vision. Our method aims to capture the best-exposed image which can boost the performance of various computer vision and robotics tas... 详细信息
来源: 评论
Learning Residual Flow as Dynamic Motion from Stereo Videos
Learning Residual Flow as Dynamic Motion from Stereo Videos
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2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
作者: Seokju Lee Sunghoon Im Stephen Lin In So Kweon Robotics and Computer Vision Laboratory KAIST Daejeon Republic of Korea Microsoft Research Asia Beijing China
We present a method for decomposing the 3D scene flow observed from a moving stereo rig into stationary scene elements and dynamic object motion. Our unsupervised learning framework jointly reasons about the camera mo...
来源: 评论
Learning residual flow as dynamic motion from stereo videos
arXiv
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arXiv 2019年
作者: Lee, Seokju Im, Sunghoon Lin, Stephen Kweon, In So Robotics and Computer Vision Laboratory KAIST Daejeon34141 Korea Republic of Microsoft Research Asia Beijing100080 China
We present a method for decomposing the 3D scene flow observed from a moving stereo rig into stationary scene elements and dynamic object motion. Our unsupervised learning framework jointly reasons about the camera mo... 详细信息
来源: 评论
Robust road marking detection & recognition using density-based grouping & machine learning techniques  17
Robust road marking detection & recognition using density-ba...
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17th IEEE Winter Conference on Applications of computer vision, WACV 2017
作者: Bailo, Oleksandr Lee, Seokju Rameau, Francois Yoon, Jae Shin Kweon, In So KAIST Robotics and Computer Vision Laboratory United States
This paper presents a robust approach for road marking detection and recognition from images captured by an embedded camera mounted on a car. Our method is designed to cope with illumination changes, shadows, and hars... 详细信息
来源: 评论
Vehicular Multi-Camera Sensor System for Automated Visual Inspection of Electric Power Distribution Equipment
Vehicular Multi-Camera Sensor System for Automated Visual In...
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2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
作者: Jinsun Park Ukcheol Shin Gyumin Shim Kyungdon Joo Francois Rameau Junhyeok Kim Dong-Geol Choi In So Kweon Robotics and Computer Vision Laboratory School of Electrical Engineering KAIST Daejeon Republic of Korea Korea Electric Power Corporation Korea Electric Power Research Institute Daejeon Republic of Korea Department of Information and Communication Engineering Hanbat National University Daejeon Republic of Korea
In this paper, we present a multi-camera sensor system along with its control algorithm for automated visual inspection from a moving vehicle. To accomplish this task, we propose a unique hardware configuration consis...
来源: 评论
Deep representation of industrial components using simulated images
Deep representation of industrial components using simulated...
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2017 IEEE International Conference on robotics and Automation, ICRA 2017
作者: Kim, Seong-Heum Choe, Gyeongmin Ahn, Byungtae Kweon, In So Robotics and Computer Vision Laboratory School of Electrical Engineering KAIST Daejeon Korea Republic of
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... 详细信息
来源: 评论