Younhyun Jung
Papers
2
Total Citations
101
H-Index
2
About
Younhyun Jung is a leading researcher at the intersection of computer vision and sports analytics, whose work is redefining how machines perceive and interact with dynamic 3D environments. His primary research areas span 3D object recognition, deep learning architectures, and video-based motion analysis. Jung’s most impactful contribution is **SparseVoxNet** (2022, 76 citations), a novel convolutional neural network that leverages sparsely aggregated 3D dense blocks for efficient and accurate 3D object recognition. This work directly addresses critical challenges in robotics and augmented reality, enabling systems to process complex 3D models with unprecedented speed and precision. In a compelling parallel, Jung has also pioneered the application of CNNs to sports science, as demonstrated in his highly cited work on **video-based table tennis tracking and trajectory prediction** (2022, 25 citations). By integrating fractal AI principles, he developed a system capable of capturing and analyzing fast-paced game events, offering a robust framework for real-time sports analytics. Jung’s dual focus on foundational 3D vision and applied sports technology showcases a rare ability to bridge theoretical innovation with tangible, real-world impact, making him a notable figure in both the AI and sports technology communities.
Research Focus
Key Achievements
Top Papers
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- 2