Jinhyung (David) Park

I am a third-year PhD student at CMU's Robotics Institute, advised by Prof. Kris Kitani. I previously received my bachelor's degree in Computer Science at CMU in 2022, also working with Prof. Kris Kitani.

I had the opportunity to conduct research at the MSC Lab in UC Berkeley for two summers, advised by Prof. Masayoshi Tomizuka and Dr. Wei Zhan.

I previously interned at Meta Zurich and Meta Reality Labs working on 3D panoptic reconstruction and parametric human body modeling.

Email  /  CV  /  Scholar  /  Github

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Research

I'm broadly interested in computer vision, joint 2D/3D understanding, human motion modeling, and multi-modal learning. Much of my research focuses on bridging 2D and 3D representations for a cohesive understanding of the world.

Generalizable Neural Human Renderer
Mana Masuda, Jinhyung Park, Shun Iwase, Rawal Khirodkar, Kris Kitani
MIRU (Meeting on Image Recognition and Understanding), 2024
paper / bibtex

Novel-view synthesis of drivable human avatars without per-subject optimization.

Flexible Depth Completion for Sparse and Varying Point Densities
Jinhyung Park, Yu-Jhe Li, Kris Kitani
CVPR, 2024
paper / bibtex

Aligning predicted depth maps with observed depth points by propagating depth corrections improves depth completion for sparse and varying input point densities.

Azimuth Super-Resolution for FMCW Radar in Autonomous Driving
Yu-Jhe Li, Shawn Hunt, Jinhyung Park, Matthew O'Toole, Kris Kitani
CVPR, 2023
paper / code / bibtex

Super-resolution of radar using raw ADC signals effectively simulates additional receiver antennas and improves downstream detection performance.

Time Will Tell: New Outlooks and A Baseline for Temporal Multi-View 3D Object Detection
Jinhyung Park*, Chenfeng Xu*, Shijia Yang, Kurt Keutzer, Kris Kitani, Masayoshi Tomizuka, Wei Zhan
ICLR, 2023   (Oral Presentation, Top 5% of accepted papers)
paper / code / bibtex

Combining long-term, low-resolution and short-term, high-resolution matching for temporal stereo yields efficient and performant camera-only 3D detectors.

DetMatch: Two Teachers are Better Than One for Joint 2D and 3D Semi-Supervised Object Detection
Jinhyung Park, Chenfeng Xu, Yiyang Zhou, Masayoshi Tomizuka, Wei Zhan
ECCV, 2022
paper / code / bibtex

Consistency between 2D and 3D pseudo-labels for joint 2D-3D semi-supervised learning stymies single-modality error propagation and improves performance.

Modality-Agnostic Learning for Radar-Lidar Fusion in Vehicle Detection
Yu-Jhe Li, Jinhyung Park, Matthew O'Toole, Kris Kitani
CVPR, 2022
paper / bibtex

Multi-modal fusion with prediction consistency between privileged teacher and noisy student alleivates collapse in difficult capture conditions and improves performance in ideal conditions.

Multi-Modality Task Cascade for 3D Object Detection
Jinhyung Park, Xinshuo Weng, Yunze Man, Kris Kitani
BMVC, 2021
paper / bibtex

Recursive, cascaded fusion of 2D and 3D representations at the task level improves both 2D segmentation and 3D detection quality.

Crack Detection and Refinement Via Deep Reinforcement Learning
Jinhyung Park, Yi-Chun Chen, Yu-Jhe Li, Kris Kitani
ICIP, 2021   (Best Industry Impact Award)
paper / bibtex

Second-stage refinement of segmentation masks through an RL agent iteratively completes and cleans predictions.

A Large-Scale Comprehensive Perception Dataset with High-Density Long-Range Point Clouds
Xinshuo Weng, Dazhi Cheng, Yunze Man, Jinhyung Park, Matthew O'Toole, Kris Kitani
arXiv, 2021
dataset page / paper / bibtex

Large-scale synthetic driving dataset with comprehensive data distribution, sensor suite, and annotations.

Protecting User Privacy: Obfuscating Discriminative Spatio-Temporal Footprints
Jinhyung Park, Erik Seglem, Eric Lin, Andreas Züfle
SIGSPATIAL LocalRec Workshop, 2017
paper / bibtex

Consideration of entropy-based and adversarial obfuscation of user geolocation trajectories for online identity protection.

Real-Time Bayesian Micro-Analysis for Metro Traffic Prediction
Eric Lin, Jinhyung Park, Andreas Züfle
SIGSPATIAL UrbanGIS Workshop, 2017
paper / bibtex

Metro outflow prediction based on estimated distribution of origin-destination station pairs.


Design and source code from Jon Barron's website