75 citations · 125 across the 7 of their papers we have counts for
7 papers
Two-Stage Grasping: A New Bin Picking Framework for Small Objects
Hanwen Cao, Jianshu Zhou, Junda Huang +5
This paper proposes a novel bin picking framework, two-stage grasping, aiming at precise grasping of cluttered small objects. Object density estimation and rough grasping are condu…
Fuzzy-Depth Objects Grasping Based on FSG Algorithm and a Soft Robotic Hand
Hanwen Cao, Junda Huang, Yichuan Li +2
Autonomous grasping is an important factor for robots physically interacting with the environment and executing versatile tasks. However, a universally applicable, cost-effective,…
SuctionNet-1Billion: A Large-Scale Benchmark for Suction Grasping
Hanwen Cao, Hao-Shu Fang, Wenhai Liu +1
Suction is an important solution for the longstanding robotic grasping problem. Compared with other kinds of grasping, suction grasping is easier to represent and often more reliab…
TDAF: Top-Down Attention Framework for Vision Tasks
Bo Pang, Yizhuo Li, Jiefeng Li +3
Human attention mechanisms often work in a top-down manner, yet it is not well explored in vision research. Here, we propose the Top-Down Attention Framework (TDAF) to capture top-…
ASAP-Net: Attention and Structure Aware Point Cloud Sequence Segmentation
Hanwen Cao, Yongyi Lu, Cewu Lu +3
Recent works of point clouds show that mulit-frame spatio-temporal modeling outperforms single-frame versions by utilizing cross-frame information. In this paper, we further improv…
Complex Sequential Understanding through the Awareness of Spatial and Temporal Concepts
Bo Pang, Kaiwen Zha, Hanwen Cao +3
Understanding sequential information is a fundamental task for artificial intelligence. Current neural networks attempt to learn spatial and temporal information as a whole, limite…
Deep RNN Framework for Visual Sequential Applications
Bo Pang, Kaiwen Zha, Hanwen Cao +2
Extracting temporal and representation features efficiently plays a pivotal role in understanding visual sequence information. To deal with this, we propose a new recurrent neural…