Dongki Jung

dongki.jung@naverlabs.com | jdk9405@gmail.com

I joined NAVER LABS in April 2021.

In NAVER LABS I've worked on computer vision, robotics and graphics.
I graduated with my MS at KAIST, where I was advised by Changick Kim.
I did my bachelors at the Korea University.

Email / CV / Google Scholar /

Research

I'm interested in the research for combining classical geometry and recent deep learning methods for 3D vision.

	WayIL: Image-based Indoor Localization with Wayfinding Maps Obin Kwon, Dongki Jung, Youngji Kim, Soohyun Ryu, Suyong Yeon, Songhwai Oh, Donghwan Lee Accepted to ICRA, 2024 We address robot localization in large-scale indoor environments using wayfinding maps.
	TMO: Textured Mesh Acquisition of Objects with a Mobile Device by using Differentiable Rendering Jaehoon Choi, Dongki Jung, Taejae Lee, Sangwook Kim, Youngdong Jung, Dinesh Manocha, Donghwan Lee Accepted to CVPR, 2023 We present a new pipeline for acquiring a textured mesh in the wild with a mobile device.
	SelfTune: Metrically Scaled Monocular Depth Estimation through Self-Supervised Learning Jaehoon Choi, Dongki Jung, Yonghan Lee, Deokhwa Kim, Dinesh Manocha, Donghwan Lee Accepted to ICRA, 2022 We have developed a fine-tuning method for metrically accurate depth estimation in a self-supervised way.
	DnD: Dense Depth Estimation in Crowded Dynamic Indoor Scenes Dongki Jung, Jaehoon Choi, Yonghan Lee, Deokhwa Kim, Changick Kim, Dinesh Manocha, Donghwan Lee Accepted to ICCV, 2021 We present a novel approach for estimating depth from a monocular camera as it moves through complex and crowded indoor environments.
	Just a Few Points are All You Need for Multi-view Stereo: A Novel Semi-supervised Learning Method for Multi-view Stereo Taekyung Kim, Jaehoon Choi, Seokeon Choi, Dongki Jung, Changick Kim Accepted to ICCV, 2021 We first introduce a novel semi-supervised multi-view stereo framework.
	SelfDeco: Self-Supervised Monocular Depth Completion in Challenging Indoor Environments Jaehoon Choi, Dongki Jung, Yonghan Lee, Deokhwa Kim, Dinesh Manocha, Donghwan Lee Accepted to ICRA, 2021 We present a novel algorithm for self-supervised monocular depth completion in challenging indoor environments.
	SAFENet: Self-Supervised Monocular Depth Estimation with Semantic-Aware Feature Extraction Jaehoon Choi, Dongki Jung, Donghwan Lee, Changick Kim NeurIPS Workshop on Machine Learning for Autonomous Driving, 2020 We propose SAFENet that is designed to leverage semantic information to overcome the limitations of the photometric loss.
	Arbitrary Style Transfer Using Graph Instance Normalization Dongki Jung, Seunghan Yang, Jaehoon Choi, Changick Kim ICIP, 2020 We present a novel learnable normalization technique for style transfer using graph convolutional networks.
	PARTIAL DOMAIN ADAPTATION USING GRAPH CONVOLUTIONAL NETWORKS Seunghan Yang, Youngeun Kim, Dongki Jung, Changick Kim arXiv, 2020 We propose a graph partial domain adaptation network, which exploits Graph Convolutional Networks.

Thanks to Jon Barron!