Xinwei Sun
Papers
1
Total Citations
3
H-Index
1
About
Xinwei Sun is a leading researcher in robotic perception and computer vision, with a primary focus on enabling robots to interact intelligently with complex, cluttered environments. His work centers on amodal segmentation and attention-guided deep learning, tackling the fundamental challenge of how machines can infer the full shape of objects even when they are partially hidden from view. Sun’s most notable contribution, the LAC-Net (Linear-Fusion Attention-Guided Convolutional Network), introduced a novel architecture that fuses linear attention mechanisms with convolutional layers to achieve accurate robotic grasping under occlusion. This work, published in 2024, has already garnered 3 citations, reflecting its immediate relevance to the field. By addressing the critical gap between segmenting visible object parts and inferring complete object geometries, Sun’s research directly advances the practicality of autonomous manipulation in real-world settings. His achievements are particularly significant for students and researchers working at the intersection of deep learning and robotics, offering a pathway toward more robust, occlusion-aware systems that can operate reliably in dynamic, unstructured environments.
Research Focus
Key Achievements
Top Papers
- 1