Chonghyuk (Andrew) Song
I'm a 2nd-year master's student at CMU's Robotics Institute, where I am extremely fortunate to be co-advised by Deva Ramanan and Jun-Yan Zhu. My research interests include 3D reconstruction of in-the-wild dynamic scenes from video, and its application to robotics.
Previously, I was at the Agency for Defense Development (ADD), where I developed autonomous mobile robots as part of my mandatory military service. I graduated from KAIST with a B.S. in Mechanical Engineering.
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Distinctiveness Oriented Positional Equilibrium for Point Cloud Registration
Taewon Min,
Chonghyuk Song,
Eunseok Kim,
Inwook Shim
ICCV, 2021
[paper]
A novel positional embedding module that yields unprecedented improvements in the accuracy of existing GNN-based rigid point cloud registration methods.
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Improving Gradient Flow with Unrolled Highway Expectation Maximization
Chonghyuk Song,
Eunseok Kim,
Inwook Shim
AAAI, 2021
[arXiv]
Replacing unrolled expectation maximization (EM) layers in neural networks with generalized EM layers based on the Newton-Rahpson method introduces highway connections, resulting in improved gradient flow during backpropagation.
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Efficient Design Space Exploration of Multi-Mode, Two-Planetary-Gear, Power-Split Hybrid Electric Powertrains via Virtual Levers
Chonghyuk Song,
Jaeho Hwang,
Dongsuk Kum
IEEE TITS, 2020
[paper]
A highly efficient design methodology that finds the optimal multi-mode, two-planetary-gear powertrain by leveraging the virtual lever, a modeling tool that eliminates the redundancy in the physical design space.
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