Zobrazeno 1 - 10
of 2 148
pro vyhledávání: '"Zhao, Lijun"'
Autor:
Li, Xiaoyu, Li, Peidong, Zhao, Lijun, Liu, Dedong, Gao, Jinghan, Wu, Xian, Wu, Yitao, Cui, Dixiao
3D Multi-Object Tracking (MOT) obtains significant performance improvements with the rapid advancements in 3D object detection, particularly in cost-effective multi-camera setups. However, the prevalent end-to-end training approach for multi-camera t
Externí odkaz:
http://arxiv.org/abs/2409.11749
Autor:
Jing, Hao, Wang, Anhong, Zhao, Lijun, Yang, Yakun, Bu, Donghan, Zhang, Jing, Zhang, Yifan, Hou, Junhui
In autonomous driving, LiDAR sensors are vital for acquiring 3D point clouds, providing reliable geometric information. However, traditional sampling methods of preprocessing often ignore semantic features, leading to detail loss and ground point int
Externí odkaz:
http://arxiv.org/abs/2407.05769
Autor:
Yu, Wenlu, Xu, Jie, Zhao, Chengwei, Zhao, Lijun, Nguyen, Thien-Minh, Yuan, Shenghai, Bai, Mingming, Xie, Lihua
LiDAR odometry is a pivotal technology in the fields of autonomous driving and autonomous mobile robotics. However, most of the current works focus on nonlinear optimization methods, and still existing many challenges in using the traditional Iterati
Externí odkaz:
http://arxiv.org/abs/2407.02190
3D Multi-Object Tracking (MOT) captures stable and comprehensive motion states of surrounding obstacles, essential for robotic perception. However, current 3D trackers face issues with accuracy and latency consistency. In this paper, we propose Fast-
Externí odkaz:
http://arxiv.org/abs/2403.13443
Publikováno v:
Xibei zhiwu xuebao, Vol 44, Iss 11, Pp 1682-1691 (2024)
[Objective] The study aims to explore the growth and physiological response of Canavalia maritima seedlings to water stress, and provide a reference for water management during the seedling stage and for the cultivation of high-quality plants in co
Externí odkaz:
https://doaj.org/article/3212a35aefe24bd291e053e4b63b3f5b
Autor:
Xie, Tao, Dai, Kun, Lu, Siyi, Wang, Ke, Jiang, Zhiqiang, Gao, Jinghan, Liu, Dedong, Xu, Jie, Zhao, Lijun, Li, Ruifeng
In this work, we seek to predict camera poses across scenes with a multi-task learning manner, where we view the localization of each scene as a new task. We propose OFVL-MS, a unified framework that dispenses with the traditional practice of trainin
Externí odkaz:
http://arxiv.org/abs/2308.11928
Autor:
Li, Xiaoyu, Xie, Tao, Liu, Dedong, Gao, Jinghan, Dai, Kun, Jiang, Zhiqiang, Zhao, Lijun, Wang, Ke
3D Multi-object tracking (MOT) empowers mobile robots to accomplish well-informed motion planning and navigation tasks by providing motion trajectories of surrounding objects. However, existing 3D MOT methods typically employ a single similarity metr
Externí odkaz:
http://arxiv.org/abs/2307.16675
Local feature matching is an essential component in many visual applications. In this work, we propose OAMatcher, a Tranformer-based detector-free method that imitates humans behavior to generate dense and accurate matches. Firstly, OAMatcher predict
Externí odkaz:
http://arxiv.org/abs/2302.05846
Local feature matching between images remains a challenging task, especially in the presence of significant appearance variations, e.g., extreme viewpoint changes. In this work, we propose DeepMatcher, a deep Transformer-based network built upon our
Externí odkaz:
http://arxiv.org/abs/2301.02993
Coherence, discord and geometric measure of entanglement are important tools for measuring physical resources. We compute them at every steps of the Grover's algorithm. We summarize these resources's patterns of change. These resources are getting sm
Externí odkaz:
http://arxiv.org/abs/2212.13938