Robotics and Computer Vision Lab

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저 자 Namil Kim, Yukyung Choi, Soonmin Hwang, In So Kweon
학 회 Association for the Advancement of Artificial Intelligence (AAAI)
Notes This work was supported by the Development of Autonomous Emergency Braking System for Pedestrian Protection project funded by the Ministry of Trade, Industry and Energy of Korea (MOTIE)(No.10044775). We also supported to gold prize from 23th HumanTech Paper Award in Samsung.
논문일시(Year) 2018
논문일시(Month) 02
http://multispectral.kaist.ac.kr** The first and second authors contributed equally to this work.

To understand the real-world, it is essential to perceive in all-day conditions including cases which are not suitable for RGB sensors, especially at night. Beyond these limitations, the innovation introduced here is a multispectral solution in the form of depth estimation from an illumination- invariant thermal sensor without an additional depth sensor. Based on an analysis of multispectral properties and the relevance to depth predictions, we propose an efficient and novel multi-task framework called the Multispectral Transfer Net- work (MTN) to estimate a depth image from a single thermal image. By exploiting geometric priors and chromaticity clues, our model can generate a pixel-wise depth image in an unsupervised manner. Moreover, we propose a new type of multitask module called Interleaver as a means of incorporating the chromaticity and fine details of skip-connections into the depth estimation framework without sharing feature layers. Lastly, we explain a novel technical means of stably training and covering large disparities and extending thermal images to data-driven methods for all-day conditions. In experiments, we demonstrate the better performance and generalization of depth estimation through the proposed multispectral stereo dataset, including various driving conditions.

List of Articles
551. Robust Low-rank Optimization with Priors
Tae-Hyun Oh
KAIST 2017 / 5
550. EPINET: A Fully-Convolutional Neural Network using Epipolar Geometry for Depth from Light Field Images
Changha Shin, Hae-Gon Jeon, Youngjin Yoon, In So Kweon, Seon Joo Kim
IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2018 / 06
549. Distort-and-Recover: Color Enhancement using Deep Reinforcement Learning
Jongchan Park, Joon-Young Lee, Donggeun Yoo, In So Kweon
IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2018 / 06
548. Robust Depth Estimation from Auto Bracketed Images
Sunghoon Im, Hae-Gon Jeon, In So Kweon
IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2018 / 06
547. Learning to Localize Sound Source in Visual Scenes
Arda Senocak, Tae-Hyun Oh, Junsik Kim, Ming-Hsuan Yang, In So Kweon
IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2018 2018 / 06
546. Globally Optimal Inlier Set Maximization for Atlanta Frame Estimation
Kyungdon Joo, Tae-Hyun Oh, In So Kweon, Jean-Charles Bazin
IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2018 / 06
545. Real-Time Head Pose Estimation using Multi-Task Deep Neural Network
Byungtae Ahn, Dong-Geol Choi, Jaesik Park, In So Kweon
Robotics and Autonomous Systems 2018 / 05
544. Depth from a Light Field Image with Learning-based Matching Costs
Hae-Gon Jeon, Jaesik Park, Gyeongmin Choe, Jinsun Park, Yunsu Bok, Yu-Wing Tai and In So Kweon
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2018 / 01.15
543. Accurate 3D Reconstruction from Small Motion Clip for Rolling Shutter Cameras
Sunghoon Im, Hyowon Ha, Gyeongmin Choe, Hae-Gon Jeon, Kyungdon Joo, In So Kweon
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2018 / 03
542. KAIST Multi-Spectral Day/Night Data Set for Autonomous and Assisted Driving
Yukyung Choi, Namil Kim, Soonmin Hwang, Kibaek Park, Jae Shin Yoon, Kyunghwan An and In So Kweon
Transactions on Intelligent Transportation Systems (T-ITS) 2018 / 03
541. RANUS: RGB and NIR Urban Scene Dataset for Deep Scene Parsing
Gyeongmin Choe, Seong-heum Kim, Sunghoon Im, Joon-Young Lee, Srinivasa Narasimhan, In So Kweon
IEEE Robotics and Automation Letters (RAL) 2018 / 02
540. Contextually Customized Video Summaries via Natural Language
Jinsoo Choi, Tae-Hyun Oh, In So Kweon
IEEE Winter Conf. on Applications of Computer Vision (WACV) 2018 / 03
539. Efficient adaptive non-maximal suppression algorithms for homogeneous spatial keypoint distribution
Oleksandr Bailo , Francois Rameau , Kyungdon Joo , Jinsun Park , Oleksandr Bogdan , In So Kweon
Pattern Recognition Letters (PRL) 2018 / 02
538. Disjoint Multi-task Learning between Heterogeneous Human-centric Tasks
Dong-Jin Kim , Jinsoo Choi , Tae-Hyun Oh , Youngjin Yoon , In So Kweon
IEEE Winter Conf. on Applications of Computer Vision (WACV) 2018 / 03
537. Learning Image Representations by Completing Damaged Jigsaw Puzzles
Dahun Kim , Donghyeon Cho , Donggeun Yoo , In So Kweon
IEEE Winter Conf. on Applications of Computer Vision (WACV) 2018 / 05
536. StairNet: Top-Down Semantic Aggregation for Accurate One Shot Detection
Sanghyun Woo, Soonmin Hwang, In So Kweon
IEEE Winter Conf. on Applications of Computer Vision (WACV) 2018 / 03
» Multispectral Transfer Network: Unsupervised Depth Estimation for All-day Vision
Namil Kim, Yukyung Choi, Soonmin Hwang, In So Kweon
Association for the Advancement of Artificial Intelligence (AAAI) 2018 / 02
534. Co-domain Embedding using Deep Quadruplet Network for Unseen Traffic Sign Recognition
Junsik Kim, Seokju Lee, Tae-Hyun Oh, In So Kweon
Association for the Advancement of Artificial Intelligence (AAAI) 2018 / 02
533. On-line Initialization and Extrinsic Calibration of an Inertial Navigation System with a Relative Preintegration Method on Manifold
Dongshin Kim, Seunghak Shin, In So Kweon
IEEE Transactions on Automation Science and Engineering (TASE) 2017 / 11
532. Robust and Globally Optimal Manhattan Frame Estimation in Near Real Time
Kyungdon Joo, Tae-Hyun Oh, Junsik Kim, In So Kweon
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2017 / 11
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