Robotics and Computer Vision Lab

Publications

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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
625. Correlate-and-Excite: Real-Time Stereo Matching via Guided Cost Volume Excitation
Antyanta Bangunharcana, Jae Won Cho, Seokju Lee, In So Kweon, Kyung-Soo Kim, Soohyun Kim
International Conference on Intelligent Robots and Systems, IROS, 2021 2021 / 06
624. Deep Volumetric Depth Fusion for 3D Scene Reconstruction
Jaesung Choe, Sunghoon Im, Francois Rameau, Minjun Kang, and In So Kweon
IEEE International Conference on Computer Vision (ICCV) 2021 / 10
623. Attentive and Contrastive Learning for Joint Depth and Motion Field Estimation
Seokju Lee, Francois Rameau, Fei Pan, and In So Kweon
IEEE International Conference on Computer Vision (ICCV) 2021 / 10
622. LabOR: Labeling Only if Required for Domain Adaptive Semantic Segmentation
Inkyu Shin, Dong-Jin Kim, Jae Won Cho, Sanghyun Woo, Kwanyong Park, and In So Kweon
IEEE International Conference on Computer Vision (ICCV) 2021 / 10
621. MS-UDA:Multi-Spectral Unsupervised Domain Adaptation for Thermal Image Semantic Segmentation
Yeong-Hyeon Kim, Ukcheol Shin, Jinsun Park, In So Kweon
IEEE Robotics and Automation Letters 2021 / 06
620. Depth Completion using Plane-Residual Representation
Byeong-Uk Lee, Kyunghyun Lee and In So Kweon
Computer Vision and Pattern Recognition, CVPR, 2021 2021 / 06
619. Learning to Associate Every Segment for Video Panoptic Segmentation
Sanghyun Woo, Dahun Kim, Joon-Young Lee and In So Kweon
Computer Vision and Pattern Recognition, CVPR, 2021 2021 / 06
618. Volumetric Propagation Network: Stereo-LiDAR Fusion for Long Range Depth Estimation
Jaesung Choe, Kyungdon Joo, Imtiaz Tooba, In So Kweon
IEEE Robotics and Automation Letters (RA-L) 2021 / 06
617. Stereo Object Matching Network
{Jaesung Choe, Kyungdon Joo}*, Francois Rameau, and In So Kweon
IEEE International Conference on Robotics and Automation (ICRA) 2021 / 06
616. Universal Adversarial Perturbations Through the Lens of Deep Steganography: Towards A Fourier Perspective
{Chaoning Zhang, Philipp Benz}*, Adil Karjauv, In So Kweon
Association for the Advancement of Artificial Intelligence (AAAI) 2021 / 02
615. Optical Flow Estimation from a Single Motion-blurred Image
Dawit Mureja Argaw, Junsik Kim, Francois Rameau, Jae Won Cho, In So Kweon
Association for the Advancement of Artificial Intelligence (AAAI) 2021 / 02
614. Motion-blurred Video Interpolation and Extrapolation
Dawit Mureja Argaw, Junsik Kim, Francois Rameau, In So Kweon
Association for the Advancement of Artificial Intelligence (AAAI) 2021 / 02
613. Learning Monocular Depth in Dynamic Scenes via Instance-Aware Projection Consistency
Seokju Lee, Sunghoon Im, Stephen Lin, In So Kweon
Association for the Advancement of Artificial Intelligence (AAAI) 2021 / 02
612. ResNet or DenseNet? Introducing Dense Shortcuts to ResNet
Chaoning Zhang*, Philipp Benz*, Dawit Mureja Argaw, Seokju Lee, Junsik Kim, Francois Rameau, Jean-Charles Bazin, In So Kweon (*: equal contribution)
IEEE Winter Conference on Applications of Computer Vision (WACV) 2021 / 1
611. High-quality Frame Interpolation via Tridirectional Inference
Jinsoo Choi, Jaesik Park, and In So Kweon
IEEE Winter Conference on Applications of Computer Vision (WACV) 2021 / 01
610. Revisiting Batch Normalization for Improving Corruption Robustness
Philipp Benz*, Chaoning Zhang*, Adil Karjauv, and In So Kweon (*: equal contribution)
IEEE Winter Conference on Applications of Computer Vision (WACV) 2021 / 01
609. The Devil is in the Boundary: Exploiting Boundary Representation for Basis-based Instance Segmentation
Myungchul Kim, Sanghyun Woo, Dahun Kim, and In So Kweon
IEEE Winter Conference on Applications of Computer Vision (WACV) 2021 / 01
608. UDH: Universal Deep Hiding for Steganography, Watermarking, and Light Field Messaging
Chaoning Zhang*, Philipp Benz*, Adil Karjauv*, Geng Sun, In-So Kweon (*: equal-contribution)
NeurIPS, 2020 2020 / 12
607. Discover, Hallucinate, and Adapt: Open Compound Domain Adaptation for Semantic Segmentation
KwanYong Park, Sanghyun Woo, Inkyu Shin, In So Kweon
NeurIPS, 2020 2020 / 12
606. An Efficient Asynchronous Method for Integrating Evolutionary and Gradient-based Policy Search
Kyunghyun Lee, Byeong-Uk Lee, Ukcheol Shin, In So Kweon
NeurIPS, 2020 2020 / 12
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