Zobrazeno 1 - 10
of 126
pro vyhledávání: '"Liu, Kangcheng"'
Visual Odometry (VO) is vital for the navigation of autonomous systems, providing accurate position and orientation estimates at reasonable costs. While traditional VO methods excel in some conditions, they struggle with challenges like variable ligh
Externí odkaz:
http://arxiv.org/abs/2404.04677
Autor:
Liu, Kangcheng
Autonomous robot navigation within the dynamic unknown environment is of crucial significance for mobile robotic applications including robot navigation in last-mile delivery and robot-enabled automated supplies in industrial and hospital delivery ap
Externí odkaz:
http://arxiv.org/abs/2401.17083
Autor:
Liu, Kangcheng
Existing state-of-the-art 3D point clouds understanding methods only perform well in a fully supervised manner. To the best of our knowledge, there exists no unified framework which simultaneously solves the downstream high-level understanding tasks,
Externí odkaz:
http://arxiv.org/abs/2312.02208
Autor:
Liu, Kangcheng
Existing state-of-the-art 3D point cloud understanding methods merely perform well in a fully supervised manner. To the best of our knowledge, there exists no unified framework that simultaneously solves the downstream high-level understanding tasks
Externí odkaz:
http://arxiv.org/abs/2312.01262
Deep neural network models have achieved remarkable progress in 3D scene understanding while trained in the closed-set setting and with full labels. However, the major bottleneck for current 3D recognition approaches is that they do not have the capa
Externí odkaz:
http://arxiv.org/abs/2312.00663
Autor:
Yin, Pengyu, Cao, Haozhi, Nguyen, Thien-Minh, Yuan, Shenghai, Zhang, Shuyang, Liu, Kangcheng, Xie, Lihua
One-shot LiDAR localization refers to the ability to estimate the robot pose from one single point cloud, which yields significant advantages in initialization and relocalization processes. In the point cloud domain, the topic has been extensively st
Externí odkaz:
http://arxiv.org/abs/2309.08914
Path planning for multiple tethered robots is a challenging problem due to the complex interactions among the cables and the possibility of severe entanglements. Previous works on this problem either consider idealistic cable models or provide no gua
Externí odkaz:
http://arxiv.org/abs/2305.00271
3D Semantic Segmentation in the Wild: Learning Generalized Models for Adverse-Condition Point Clouds
Autor:
Xiao, Aoran, Huang, Jiaxing, Xuan, Weihao, Ren, Ruijie, Liu, Kangcheng, Guan, Dayan, Saddik, Abdulmotaleb El, Lu, Shijian, Xing, Eric
Robust point cloud parsing under all-weather conditions is crucial to level-5 autonomy in autonomous driving. However, how to learn a universal 3D semantic segmentation (3DSS) model is largely neglected as most existing benchmarks are dominated by po
Externí odkaz:
http://arxiv.org/abs/2304.00690
Autor:
Zhao, Yuzhi, Po, Lai-Man, Liu, Kangcheng, Wang, Xuehui, Yu, Wing-Yin, Xian, Pengfei, Zhang, Yujia, Liu, Mengyang
In this paper, we propose a scribble-based video colorization network with temporal aggregation called SVCNet. It can colorize monochrome videos based on different user-given color scribbles. It addresses three common issues in the scribble-based vid
Externí odkaz:
http://arxiv.org/abs/2303.11591
Contrastive learning has recently demonstrated great potential for unsupervised pre-training in 3D scene understanding tasks. However, most existing work randomly selects point features as anchors while building contrast, leading to a clear bias towa
Externí odkaz:
http://arxiv.org/abs/2303.06388