Juhyeon Kim 🦋

Juhyeon Kim

PhD student of Computer Graphics

Dartmouth College

I am a second year computer science PhD student at Dartmouth College. My research interest includes physically based rendering, real-time rendering and neural rendering. Currently I am researching ToF rendering. I received master’s degree from Seoul National University 3D vision lab. I also received bachelor’s degree from Seoul National University.

News

  • 2024.06. I will be working at Intel for summer internship!
  • 2024.05. One ICCP paper accepted!

Education

 
 
 
 
 
Dartmouth College
PhD in Computer Science
Dartmouth College
September 2022 – Present Hanover, New Hampshire
 
 
 
 
 
Seoul National University
MSc in Electrical and Computer Engineering
Seoul National University
September 2019 – February 2022 Seoul
  • Thesis : Fast and lightweight Path Guiding Algorithm on GPU
 
 
 
 
 
Seoul National University
BSc in College of Liberal Arts
Seoul National University
March 2014 – August 2019 Seoul
  • Major in Electrical and Computer Engineering
  • Thesis : Efficient Taxi Dispatch Strategy using Deep Reinforcement Learning

Experience

 
 
 
 
 
Intel
Summer Intern
Intel
June 2024 – Present Bellevue, Washington

Gallery

Featured Publications

Doppler Time-of-Flight Rendering
Doppler Time-of-Flight Rendering
IBL-NeRF: Image-Based Lighting Formulation of Neural Radiance Fields
IBL-NeRF: Image-Based Lighting Formulation of Neural Radiance Fields
Neural Volumetric Reconstruction for Coherent Synthetic Aperture Sonar
Neural Volumetric Reconstruction for Coherent Synthetic Aperture Sonar
Fast and Lightweight Path Guiding Algorithm on GPU
Fast and Lightweight Path Guiding Algorithm on GPU

Publications

Efficient Time Sampling Strategy for Transient Absorption Spectroscopy
Efficient Time Sampling Strategy for Transient Absorption Spectroscopy
Optimizing Large-Scale Fleet Management on a Road Network using Multi-Agent Deep Reinforcement Learning with Graph Neural Network
Optimizing Large-Scale Fleet Management on a Road Network using Multi-Agent Deep Reinforcement Learning with Graph Neural Network
Novel View Synthesis with Skip Connections
Novel View Synthesis with Skip Connections