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pro vyhledávání: '"Zhu, Hongzi"'
Autor:
Zhou, Yunsong, Simon, Michael, Peng, Zhenghao, Mo, Sicheng, Zhu, Hongzi, Guo, Minyi, Zhou, Bolei
Controllable synthetic data generation can substantially lower the annotation cost of training data. Prior works use diffusion models to generate driving images conditioned on the 3D object layout. However, those models are trained on small-scale dat
Externí odkaz:
http://arxiv.org/abs/2406.09386
Autor:
Li, Yunzhe, Zhu, Hongzi, Deng, Zhuohong, Cheng, Yunlong, Zhang, Liang, Chang, Shan, Guo, Minyi
Emerging Artificial Intelligence of Things (AIoT) applications desire online prediction using deep neural network (DNN) models on mobile devices. However, due to the movement of devices, unfamiliar test samples constantly appear, significantly affect
Externí odkaz:
http://arxiv.org/abs/2407.03331
Autor:
Zhou, Yunsong, Huang, Linyan, Bu, Qingwen, Zeng, Jia, Li, Tianyu, Qiu, Hang, Zhu, Hongzi, Guo, Minyi, Qiao, Yu, Li, Hongyang
Embodied scene understanding serves as the cornerstone for autonomous agents to perceive, interpret, and respond to open driving scenarios. Such understanding is typically founded upon Vision-Language Models (VLMs). Nevertheless, existing VLMs are re
Externí odkaz:
http://arxiv.org/abs/2403.04593
Registration of point clouds collected from a pair of distant vehicles provides a comprehensive and accurate 3D view of the driving scenario, which is vital for driving safety related applications, yet existing literature suffers from the expensive p
Externí odkaz:
http://arxiv.org/abs/2403.03532
Registration of distant outdoor LiDAR point clouds is crucial to extending the 3D vision of collaborative autonomous vehicles, and yet is challenging due to small overlapping area and a huge disparity between observed point densities. In this paper,
Externí odkaz:
http://arxiv.org/abs/2307.09788
For many driving safety applications, it is of great importance to accurately register LiDAR point clouds generated on distant moving vehicles. However, such point clouds have extremely different point density and sensor perspective on the same objec
Externí odkaz:
http://arxiv.org/abs/2305.02893
Monocular 3D object detection (Mono3D) in mobile settings (e.g., on a vehicle, a drone, or a robot) is an important yet challenging task. Due to the near-far disparity phenomenon of monocular vision and the ever-changing camera pose, it is hard to ac
Externí odkaz:
http://arxiv.org/abs/2303.13561
Mobile monocular 3D object detection (Mono3D) (e.g., on a vehicle, a drone, or a robot) is an important yet challenging task. Existing transformer-based offline Mono3D models adopt grid-based vision tokens, which is suboptimal when using coarse token
Externí odkaz:
http://arxiv.org/abs/2303.13018
Monocular 3D object detection is an important task in autonomous driving. It can be easily intractable where there exists ego-car pose change w.r.t. ground plane. This is common due to the slight fluctuation of road smoothness and slope. Due to the l
Externí odkaz:
http://arxiv.org/abs/2106.15796
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