Papers
23
Total Citations
429
H-Index
11
About
Kailun Yang is a prominent researcher specializing in panoramic imaging, semantic scene understanding, and perception systems for autonomous driving, robotics, and assistive technologies. His work addresses a critical challenge in modern AI-driven perception: extending visual understanding beyond the narrow field of view of conventional cameras to encompass wide-angle and 360° panoramic imagery. Yang's most influential contribution, a 2022 comprehensive review on panoramic imaging and scene understanding (108 citations), has become an essential reference for researchers exploring next-generation perception systems. His pioneering work on panoramic semantic segmentation, including the DS-PASS framework, demonstrates his commitment to solving real-world challenges in autonomous transportation. He has further advanced the field through novel approaches to optical flow estimation, visual-inertial odometry, and SLAM systems specifically designed for large field-of-view cameras. Beyond autonomous driving, Yang's research extends meaningfully into humanitarian applications, developing semantic segmentation tools to assist visually impaired pedestrians at intersections and robustifying traversability perception for wearable robotics. His multimodal fusion work, spanning RGB-depth, polarization, and thermal imaging, reflects a holistic approach to robust scene cognition. With over 330 cumulative citations across diverse topics, Yang has established himself as a versatile and impactful force in intelligent perception research.
Research Focus
Key Achievements
Top Papers
- 1Review on Panoramic Imaging and Its Applications in Scene Understanding108 citations · 2022
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- 4PanoFlow: Learning 360° Optical Flow for Surrounding Temporal Understanding24 citations · 2023
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- 8Predicting Polarization Beyond Semantics for Wearable Robotics19 citations · 2018
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