Haibing Guo
Papers
2
Total Citations
7
H-Index
2
About
Haibing Guo is a researcher advancing the intersection of computer vision and robotics, with a focus on 3D perception and industrial automation. His work centers on developing deep learning architectures for point cloud instance segmentation and image-based detection in robotic manipulation tasks. In his 2023 paper "C-LFNet: Central-Local Feature 3D Point Cloud Instance Segmentation Network for Robot Bin-Picking," Guo introduced a novel network that effectively captures both global and local geometric features, enabling precise segmentation for complex bin-picking scenarios—a critical capability for autonomous manufacturing. Building on this, his 2024 work "DGConv: A Novel Convolutional Neural Network Approach for Weld Seam Depth Image Detection" tackles the challenge of robust weld seam recognition in robotic welding operations, proposing an innovative segmentation algorithm that enhances geometric attribute extraction from depth images. Although early in his career with emerging citation counts (4 and 3 respectively), Guo’s contributions are directly addressing real-world industrial challenges, bridging the gap between theoretical computer vision and practical robotic intelligence. His research holds promise for advancing autonomous systems in manufacturing and assembly.
Research Focus
Key Achievements
Top Papers
- 1
- 2