Dongki Jung

jdk9405@umd.edu   |   jdk9405@gmail.com

I am a Computer Science Ph.D. student at University of Maryland, College Park (UMD), working with Prof. Dinesh Manocha at the GAMMA Lab. At UMD, I've worked on 3D reconstruction and neural rendering.

Previously, I was a research scientist at NAVER LABS. I've worked on visual localization and mapping for robotics.
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  / 

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

We propose the first learning-based dense matching algorithm for omnidirectional images.

We propose a novel 3D Gaussian Splatting algorithm that integrates monocular depth network with anchored Gaussian Splatting, enabling robust rendering performance on sparse-view datasets.

We address robot localization in large-scale indoor environments using wayfinding maps.

We present a new pipeline for acquiring a textured mesh in the wild with a mobile device.

We have developed a fine-tuning method for metrically accurate depth estimation in a self-supervised way.

We present a novel approach for estimating depth from a monocular camera as it moves through complex and crowded indoor environments.

We first introduce a novel semi-supervised multi-view stereo framework.

We present a novel algorithm for self-supervised monocular depth completion in challenging indoor environments.

We propose SAFENet that is designed to leverage semantic information to overcome the limitations of the photometric loss.

We present a novel learnable normalization technique for style transfer using graph convolutional networks.

We propose a graph partial domain adaptation network, which exploits Graph Convolutional Networks.

Thanks to Jon Barron!