Zobrazeno 1 - 10
of 73
pro vyhledávání: '"Cao Ziang"'
Publikováno v:
Journal of Agricultural Engineering (2024)
In the pursuit of intelligent and efficient grape picking, rapid and precise detection of grape locations serves as the fundamental cornerstone. However, amidst the natural environment, grape detection encounters various interference factors, such as
Externí odkaz:
https://doaj.org/article/2d3aaa12628840fa93fad1aa7fe2e2a6
Autor:
Chen, Zhaoxi, Tang, Jiaxiang, Dong, Yuhao, Cao, Ziang, Hong, Fangzhou, Lan, Yushi, Wang, Tengfei, Xie, Haozhe, Wu, Tong, Saito, Shunsuke, Pan, Liang, Lin, Dahua, Liu, Ziwei
The increasing demand for high-quality 3D assets across various industries necessitates efficient and automated 3D content creation. Despite recent advancements in 3D generative models, existing methods still face challenges with optimization speed,
Externí odkaz:
http://arxiv.org/abs/2409.12957
Generating diverse and high-quality 3D assets automatically poses a fundamental yet challenging task in 3D computer vision. Despite extensive efforts in 3D generation, existing optimization-based approaches struggle to produce large-scale 3D assets e
Externí odkaz:
http://arxiv.org/abs/2405.08055
Autor:
Hong, Fangzhou, Tang, Jiaxiang, Cao, Ziang, Shi, Min, Wu, Tong, Chen, Zhaoxi, Yang, Shuai, Wang, Tengfei, Pan, Liang, Lin, Dahua, Liu, Ziwei
We present a two-stage text-to-3D generation system, namely 3DTopia, which generates high-quality general 3D assets within 5 minutes using hybrid diffusion priors. The first stage samples from a 3D diffusion prior directly learned from 3D data. Speci
Externí odkaz:
http://arxiv.org/abs/2403.02234
Creating diverse and high-quality 3D assets with an automatic generative model is highly desirable. Despite extensive efforts on 3D generation, most existing works focus on the generation of a single category or a few categories. In this paper, we in
Externí odkaz:
http://arxiv.org/abs/2309.07920
Visual tracking has made significant improvements in the past few decades. Most existing state-of-the-art trackers 1) merely aim for performance in ideal conditions while overlooking the real-world conditions; 2) adopt the tracking-by-detection parad
Externí odkaz:
http://arxiv.org/abs/2308.10330
Autor:
Huang, Peide, Zhang, Xilun, Cao, Ziang, Liu, Shiqi, Xu, Mengdi, Ding, Wenhao, Francis, Jonathan, Chen, Bingqing, Zhao, Ding
Training control policies in simulation is more appealing than on real robots directly, as it allows for exploring diverse states in an efficient manner. Yet, robot simulators inevitably exhibit disparities from the real-world \rebut{dynamics}, yield
Externí odkaz:
http://arxiv.org/abs/2306.15864
Publikováno v:
IEEE Robotics and Automation Letters, 2022, vol. 7 No. 2
Most previous progress in object tracking is realized in daytime scenes with favorable illumination. State-of-the-arts can hardly carry on their superiority at night so far, thereby considerably blocking the broadening of visual tracking-related unma
Externí odkaz:
http://arxiv.org/abs/2303.10951
Transformer-based visual object tracking has been utilized extensively. However, the Transformer structure is lack of enough inductive bias. In addition, only focusing on encoding the global feature does harm to modeling local details, which restrict
Externí odkaz:
http://arxiv.org/abs/2208.00662
Unmanned aerial vehicle (UAV)-based visual object tracking has enabled a wide range of applications and attracted increasing attention in the field of intelligent transportation systems because of its versatility and effectiveness. As an emerging for
Externí odkaz:
http://arxiv.org/abs/2205.04281