12 citations · 16 across the 9 of their papers we have counts for
4 papers · 1 filter
TODE-Trans: Transparent Object Depth Estimation with Transformer
Kang Chen, Shaochen Wang, Beihao Xia +3
Transparent objects are widely used in industrial automation and daily life. However, robust visual recognition and perception of transparent objects have always been a major chall…
A Robotic Visual Grasping Design: Rethinking Convolution Neural Network with High-Resolutions
Zhangli Zhou, Shaochen Wang, Ziyang Chen +2
High-resolution representations are important for vision-based robotic grasping problems. Existing works generally encode the input images into low-resolution representations via s…
What You See is What You Grasp: User-Friendly Grasping Guided by Near-eye-tracking
Shaochen Wang, Wei Zhang, Zhangli Zhou +5
This work presents a next-generation human-robot interface that can infer and realize the user's manipulation intention via sight only. Specifically, we develop a system that integ…
When Transformer Meets Robotic Grasping: Exploits Context for Efficient Grasp Detection
Shaochen Wang, Zhangli Zhou, Zhen Kan
In this paper, we present a transformer-based architecture, namely TF-Grasp, for robotic grasp detection. The developed TF-Grasp framework has two elaborate designs making it well…