Xuejian Gong
Papers
1
Total Citations
11
H-Index
1
About
Xuejian Gong is a researcher whose work lies at the intersection of computer vision and robotic manipulation, with a particular focus on 3D point cloud processing for industrial automation. His key research area involves developing efficient algorithms for object segmentation and recognition in bin-picking systems, where robots must grasp randomly oriented parts from a bin. Gong’s major contribution is a novel, computationally efficient method for segmenting 3D scattered parts from point cloud data captured by low-cost depth sensors like the Intel RealSense. His 2017 paper on this topic, which has garnered 11 citations, addresses the critical challenge of filtering noisy, disordered point clouds to enable reliable object identification. This work is notable for its practical approach, demonstrating that effective segmentation can be achieved without expensive hardware, making bin-picking more accessible for industrial applications. Gong’s research bridges the gap between theoretical computer vision and real-world robotic systems, offering solutions that are both robust and cost-effective. His contributions are particularly valuable for students and researchers exploring the intersection of 3D sensing, point cloud processing, and automated manufacturing.
Research Focus
Key Achievements
Top Papers
- 1Point cloud segmentation of 3D scattered parts sampled by RealSense11 citations · 2017