Papers
11
Total Citations
139
H-Index
6
About
Qieshi Zhang is a leading researcher in computer vision and robotics, with a primary focus on 6D pose estimation, simultaneous localization and mapping (SLAM), and intelligent robotic systems. His most significant contributions lie in developing real-time, efficient methods for 6D object pose estimation from single RGB images, a critical technology for robotics manipulation, autonomous driving, and augmented reality. His highly cited work, including "Real-Time and Efficient 6-D Pose Estimation From a Single RGB Image" (40 citations) and "BDR6D: Bidirectional Deep Residual Fusion Network for 6D Pose Estimation" (21 citations), has advanced the field by enabling direct prediction of 2D keypoints for robust pose determination. Zhang has also made notable strides in low-light visual-inertial SLAM with "LMVI-SLAM" (19 citations), addressing a key challenge for robots operating in challenging environments. His research extends to cognition-based cloud computing for mobile education robots and self-supervised depth estimation, demonstrating a broad impact on autonomous systems. With over 130 total citations, Zhang's work is foundational for next-generation robotic perception and navigation.
Research Focus
Key Achievements
Top Papers
- 1Real-Time and Efficient 6-D Pose Estimation From a Single RGB Image40 citations · 2021
- 2BDR6D: Bidirectional Deep Residual Fusion Network for 6D Pose Estimation21 citations · 2023
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- 5MFPN-6D : Real-time One-stage Pose Estimation of Objects on RGB Images13 citations · 2021
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- 10A Lifted Semi-Direct Monocular Visual Odometry2 citations · 2019