Zobrazeno 1 - 10
of 21 431
pro vyhledávání: '"WEI, Yi"'
Publikováno v:
Ciência Rural. 2023, Vol. 53 Issue 9, p1-12. 12p.
Akademický článek
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Stereo matching for inland waterways is one of the key technologies for the autonomous navigation of Unmanned Surface Vehicles (USVs), which involves dividing the stereo images into reference images and target images for pixel-level matching. However
Externí odkaz:
http://arxiv.org/abs/2410.07915
For 6-DoF grasp detection, simulated data is expandable to train more powerful model, but it faces the challenge of the large gap between simulation and real world. Previous works bridge this gap with a sim-to-real way. However, this way explicitly o
Externí odkaz:
http://arxiv.org/abs/2410.06521
Autor:
Tan, Chaolei, Lin, Zihang, Pu, Junfu, Qi, Zhongang, Pei, Wei-Yi, Qu, Zhi, Wang, Yexin, Shan, Ying, Zheng, Wei-Shi, Hu, Jian-Fang
Video grounding is a fundamental problem in multimodal content understanding, aiming to localize specific natural language queries in an untrimmed video. However, current video grounding datasets merely focus on simple events and are either limited t
Externí odkaz:
http://arxiv.org/abs/2408.01669
Robotic grasping in clutters is a fundamental task in robotic manipulation. In this work, we propose an economic framework for 6-DoF grasp detection, aiming to economize the resource cost in training and meanwhile maintain effective grasp performance
Externí odkaz:
http://arxiv.org/abs/2407.08366
In this work, we introduce the Geometry-Aware Large Reconstruction Model (GeoLRM), an approach which can predict high-quality assets with 512k Gaussians and 21 input images in only 11 GB GPU memory. Previous works neglect the inherent sparsity of 3D
Externí odkaz:
http://arxiv.org/abs/2406.15333
Autor:
Wei, Yi-Lin, Jiang, Jian-Jian, Xing, Chengyi, Tan, Xiantuo, Wu, Xiao-Ming, Li, Hao, Cutkosky, Mark, Zheng, Wei-Shi
This paper explores a novel task ""Dexterous Grasp as You Say"" (DexGYS), enabling robots to perform dexterous grasping based on human commands expressed in natural language. However, the development of this field is hindered by the lack of datasets
Externí odkaz:
http://arxiv.org/abs/2405.19291
In this work, we propose a novel discriminative framework for dexterous grasp generation, named Dexterous Grasp TRansformer (DGTR), capable of predicting a diverse set of feasible grasp poses by processing the object point cloud with only one forward
Externí odkaz:
http://arxiv.org/abs/2404.18135
In this work, we explore a novel task of generating human grasps based on single-view scene point clouds, which more accurately mirrors the typical real-world situation of observing objects from a single viewpoint. Due to the incompleteness of object
Externí odkaz:
http://arxiv.org/abs/2404.15815