Zobrazeno 1 - 10
of 20
pro vyhledávání: '"Charles R. Qi"'
Autor:
Longlong Jing, Ruichi Yu, Henrik Kretzschmar, Kang Li, Charles R. Qi, Hang Zhao, Alper Ayvaci, Xu Chen, Dillon Cower, Yingwei Li, Yurong You, Han Deng, Congcong Li, Dragomir Anguelov
Publikováno v:
2022 International Conference on Robotics and Automation (ICRA).
Monocular image-based 3D perception has become an active research area in recent years owing to its applications in autonomous driving. Approaches to monocular 3D perception including detection and tracking, however, often yield inferior performance
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::41a1058810886f1c2a1b0f47d321310b
https://doi.org/10.1109/icra46639.2022.9811749
https://doi.org/10.1109/icra46639.2022.9811749
Autor:
Chenxi Liu, Zhaoqi Leng, Pei Sun, Shuyang Cheng, Charles R. Qi, Yin Zhou, Mingxing Tan, Dragomir Anguelov
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783031198021
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7320740e4f673ee65a3f483226b865be
https://doi.org/10.1007/978-3-031-19803-8_10
https://doi.org/10.1007/978-3-031-19803-8_10
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783031198410
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fe30a48e105a0bf7e2a7739d0fd03032
https://doi.org/10.1007/978-3-031-19842-7_5
https://doi.org/10.1007/978-3-031-19842-7_5
Autor:
Mahyar Najibi, Jingwei Ji, Yin Zhou, Charles R. Qi, Xinchen Yan, Scott Ettinger, Dragomir Anguelov
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783031198380
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c7f2ed72b6c20b6d872fb538a2d85b00
https://doi.org/10.1007/978-3-031-19839-7_25
https://doi.org/10.1007/978-3-031-19839-7_25
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783031200793
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2241aea3a82b4624e326e3c629830771
https://doi.org/10.1007/978-3-031-20080-9_10
https://doi.org/10.1007/978-3-031-20080-9_10
In autonomous driving, a LiDAR-based object detector should perform reliably at different geographic locations and under various weather conditions. While recent 3D detection research focuses on improving performance within a single domain, our study
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f69ecf05cd41a449a33f38ee081c2a7b
Publikováno v:
CVPR
While current 3D object recognition research mostly focuses on the real-time, onboard scenario, there are many offboard use cases of perception that are largely under-explored, such as using machines to automatically generate high-quality 3D labels.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ef72d2871e65642e2d4ffb69bf477d59
Publikováno v:
CVPR
3D object detection has seen quick progress thanks to advances in deep learning on point clouds. A few recent works have even shown state-of-the-art performance with just point clouds input (e.g. VoteNet). However, point cloud data have inherent limi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0e07f1991574c7797e087bd3e88dd882
http://arxiv.org/abs/2001.10692
http://arxiv.org/abs/2001.10692
Publikováno v:
Computer Vision – ECCV 2020 ISBN: 9783030585792
ECCV (3)
ECCV (3)
Arguably one of the top success stories of deep learning is transfer learning. The finding that pre-training a network on a rich source set (e.g., ImageNet) can help boost performance once fine-tuned on a usually much smaller target set, has been ins
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::09478bb8dff1ed829243597a6a706e21
https://doi.org/10.1007/978-3-030-58580-8_34
https://doi.org/10.1007/978-3-030-58580-8_34
Autor:
Charles R. Qi
Publikováno v:
3D Imaging, Analysis and Applications ISBN: 9783030440695
Emerging 3D related applications such as autonomous vehicles, AI-assisted design, and augmented reality have highlighted the demands for more robust and powerful 3D analyzing algorithms. Inspired by the success of deep learning on understanding image
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e2cbe8fa47b751041038d8e79b71a4fd
https://doi.org/10.1007/978-3-030-44070-1_11
https://doi.org/10.1007/978-3-030-44070-1_11