Zobrazeno 1 - 10
of 17
pro vyhledávání: '"Gyeongmin Choe"'
Publikováno v:
IEEE Robotics and Automation Letters. 5:2506-2513
We introduce a new method to find a salient viewpoint with a deep representation, according to ease of semantic segmentation. The main idea in our segmentation network is to utilize the multipath network with informative two views. In order to collec
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::099b4d0164482aea4a6c8e9ff43d65ec
https://doi.org/10.1109/lra.2020.2972886
https://doi.org/10.1109/lra.2020.2972886
Publikováno v:
IEEE Transactions on Pattern Analysis and Machine Intelligence. 41:775-787
Structure from small motion has become an important topic in 3D computer vision as a method for estimating depth, since capturing the input is so user-friendly. However, major limitations exist with respect to the form of depth uncertainty, due to th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c09d09d4669b0ae8c034537733cd6c7d
https://doi.org/10.1109/tpami.2018.2819679
https://doi.org/10.1109/tpami.2018.2819679
Autor:
Michael Polley, Hamid R. Sheikh, Seok-jun Lee, Gyeongmin Choe, Osama Nabil, Zeeshan Nadir, Jinhan Hu, Yoo Youngjun
Publikováno v:
CVPR Workshops
Deep learning-based mobile imaging applications are often limited by the lack of training data. To this end, researchers have resorted to using synthetic training data. However, pure synthetic data does not accurately mimic the distribution of the re
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::66f50a6f518debeb0d0d16818dbd31f0
https://doi.org/10.1109/cvprw50498.2020.00266
https://doi.org/10.1109/cvprw50498.2020.00266
Autor:
Sunghoon Im, Srinivasa G. Narasimhan, Gyeongmin Choe, Seong-heum Kim, Joon-Young Lee, In So Kweon
Publikováno v:
IEEE Robotics and Automation Letters. 3:1808-1815
In this letter, we present a data-driven method for scene parsing of road scenes to utilize single-channel near-infrared (NIR) images. To overcome the lack of data problem in non-RGB spectrum, we define a new color space and decompose the task of dee
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b6013c262b7d76566288dce50b6fd1f2
https://doi.org/10.1109/lra.2018.2801390
https://doi.org/10.1109/lra.2018.2801390
Autor:
Maximilian Diebold, Marcel Gutsche, Anna Alperovich, Ole Johannsen, Shuo Zhang, Jaesik Park, Marco Carli, Michele Brizzi, Hae-Gon Jeon, Yu-Wing Tai, Sven Wanner, Bastian Goldluecke, Jinsun Park, Yunsu Bok, Zhang Xiong, Hao Sheng, Jingyi Yu, Qing Wang, Lipeng Si, Katrin Honauer, In So Kweon, Antonin Sulc, Gyeongmin Choe, Michael Strecke, Hendrik Schilling, Hao Zhu, Federica Battisti, Ting-Chun Wang
Publikováno v:
CVPR Workshops
This paper presents the results of the depth estimation challenge for dense light fields, which took place at the second workshop on Light Fields for Computer Vision (LF4CV) in conjunction with CVPR 2017. The challenge consisted of submission to a re
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5ffa868510b01306b9d4840d36d0d24d
http://hdl.handle.net/11577/3363391
http://hdl.handle.net/11577/3363391
Publikováno v:
International Journal of Computer Vision. 122:1-16
We propose a method to refine geometry of 3D meshes from a consumer level depth camera, e.g. Kinect, by exploiting shading cues captured from an infrared (IR) camera. A major benefit to using an IR camera instead of an RGB camera is that the IR image
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::59d20b7b16e9371e7590d44d3ddc94ba
https://doi.org/10.1007/s11263-016-0937-y
https://doi.org/10.1007/s11263-016-0937-y
Publikováno v:
IEEE transactions on pattern analysis and machine intelligence. 41(2)
One of the core applications of light field imaging is depth estimation. To acquire a depth map, existing approaches apply a single photo-consistency measure to an entire light field. However, this is not an optimal choice because of the non-uniform
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2567ae17be33ccf87d850a7c323751b1
https://pubmed.ncbi.nlm.nih.gov/29994179
https://pubmed.ncbi.nlm.nih.gov/29994179
Publikováno v:
ICRA
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
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::816863b3c8a650388a4e5c025161e97d
https://doi.org/10.1109/icra.2017.7989232
https://doi.org/10.1109/icra.2017.7989232
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783319683447
ICVS
ICVS
The advanced driver assistance system (ADAS) has been actively researched to enable adaptive cruise control and collision avoidance, however, conventional ADAS is not capable of more advanced functions due to the absence of intelligent decision makin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0cd50fe0b45581d5a5ad0e5bc32d4640
https://doi.org/10.1007/978-3-319-68345-4_31
https://doi.org/10.1007/978-3-319-68345-4_31
Publikováno v:
CVPR
Near-Infrared (NIR) images of most materials exhibit less texture or albedo variations making them beneficial for vision tasks such as intrinsic image decomposition and structured light depth estimation. Understanding the reflectance properties (BRDF
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::41bab811a7f079fd4957d483a4a9ea15
https://doi.org/10.1109/cvpr.2016.269
https://doi.org/10.1109/cvpr.2016.269