Yujie Yang
Papers
2
Total Citations
11
H-Index
2
About
Yujie Yang is an emerging researcher specializing in 3D point cloud processing, semantic segmentation, and real-time robotic perception. Their work sits at the intersection of computer vision, deep learning, and robotics, with a particular focus on enabling intelligent environmental understanding for autonomous systems operating under real-world computational constraints. Yang's most notable contributions address a critical challenge in robotics: performing accurate, real-time semantic segmentation of 3D point clouds on resource-limited onboard platforms. Their 2023 paper introducing an attention mechanism combined with sparse tensor representations demonstrated a meaningful advance in balancing segmentation accuracy with computational efficiency — a persistent bottleneck for robotic systems using LiDAR or sonar sensors. A companion work extended this research to practical onboard edge device deployment, underscoring Yang's commitment to bridging theoretical models and real-world robotic applications. With a growing citation record — including 8 citations for their attention-based segmentation approach — Yang is establishing a focused research identity in embodied AI and autonomous perception. Students and researchers working on autonomous vehicles, underwater robotics, or edge-deployed AI systems will find Yang's contributions particularly relevant, as they directly tackle the computational realities that laboratory benchmarks often overlook.
Research Focus
Key Achievements
Top Papers
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- 2