Zobrazeno 1 - 10
of 49
pro vyhledávání: '"Chen, Hanzhi"'
Autor:
Chen, Haolong, Chen, Hanzhi, Zhao, Zijian, Han, Kaifeng, Zhu, Guangxu, Zhao, Yichen, Du, Ying, Xu, Wei, Shi, Qingjiang
The impressive performance of ChatGPT and other foundation-model-based products in human language understanding has prompted both academia and industry to explore how these models can be tailored for specific industries and application scenarios. Thi
Externí odkaz:
http://arxiv.org/abs/2409.04267
Autor:
Chen, Tingwei, Wang, Yantao, Chen, Hanzhi, Zhao, Zijian, Li, Xinhao, Piovesan, Nicola, Zhu, Guangxu, Shi, Qingjiang
The introduction of fifth-generation (5G) radio technology has revolutionized communications, bringing unprecedented automation, capacity, connectivity, and ultra-fast, reliable communications. However, this technological leap comes with a substantia
Externí odkaz:
http://arxiv.org/abs/2406.16929
Autor:
Laina, Sebastián Barbas, Boche, Simon, Papatheodorou, Sotiris, Tzoumanikas, Dimos, Schaefer, Simon, Chen, Hanzhi, Leutenegger, Stefan
Forestry constitutes a key element for a sustainable future, while it is supremely challenging to introduce digital processes to improve efficiency. The main limitation is the difficulty of obtaining accurate maps at high temporal and spatial resolut
Externí odkaz:
http://arxiv.org/abs/2403.09596
We present FuncGrasp, a framework that can infer dense yet reliable grasp configurations for unseen objects using one annotated object and single-view RGB-D observation via categorical priors. Unlike previous works that only transfer a set of grasp p
Externí odkaz:
http://arxiv.org/abs/2402.05644
Autor:
Hidalgo-Carvajal, Diego, Chen, Hanzhi, Bettelani, Gemma C., Jung, Jaesug, Zavaglia, Melissa, Busse, Laura, Naceri, Abdeldjallil, Leutenegger, Stefan, Haddadin, Sami
The progressive prevalence of robots in human-suited environments has given rise to a myriad of object manipulation techniques, in which dexterity plays a paramount role. It is well-established that humans exhibit extraordinary dexterity when handlin
Externí odkaz:
http://arxiv.org/abs/2311.02510
In this paper, we introduce neural texture learning for 6D object pose estimation from synthetic data and a few unlabelled real images. Our major contribution is a novel learning scheme which removes the drawbacks of previous works, namely the strong
Externí odkaz:
http://arxiv.org/abs/2212.12902
Inferring geometrically consistent dense 3D scenes across a tuple of temporally consecutive images remains challenging for self-supervised monocular depth prediction pipelines. This paper explores how the increasingly popular transformer architecture
Externí odkaz:
http://arxiv.org/abs/2110.08192
Publikováno v:
In Process Safety and Environmental Protection February 2023 170:207-214
Publikováno v:
In Chemosphere September 2023
Publikováno v:
Satellite Navigation; 10/7/2024, Vol. 5 Issue 1, p1-13, 13p