I am a senior research engineer at Hyundai Motor Company, South Korea. I received Ph.D. from Yonsei University, advised by Prof. Kwanghoon Sohn.

I am a computer vision fanatic and like the researches that could directly be applied to real-world problems. I am interested in a wide array of topics, ranging from low-level vision to high-level vision, and their connections to autonomous driving assistance systems. Recently, I have been thinking more from the 3D perspective. Here’s my CV.

Research

	Memory-guided Image Deraining using Time-laspe data Jaehoon Cho, Seungryong Kim, Kwanghoon Sohn IEEE Trans. on Image Processing (TIP), (submitted) Paper
	Wide and Narrow: Video Prediction from Context and Motion Jaehoon Cho, Jiyoung Lee, Changjae Oh, Wonil Song, Kwanghoon Sohn British Machine Vision Conference (BMVC) 2021 Paper
	Deep Monocular Depth Estimation Leveraging a Large-scale Outdoor Stereo Dataset Jaehoon Cho, Dongbo Min, Youngjung Kim, Kwanghoon Sohn Expert Systems With Applications (ESWA), vol. 178, Mar. 2021 (Impact Factor: 5.452) Project page | Paper | Dataset
	Single Image Deraining using Time-laspe data Jaehoon Cho, Seungryong Kim, Dongbo Min, Kwanghoon Sohn IEEE Trans. on Image Processing (TIP), vol. 29, pp. 7274-7289, Jun. 2020 (Impact factor: 9.340) Paper
	Pyramid Inter-Attention for High Dynamic Range Imaging Sungil Choi, Jaehoon Cho, Wonil Song, Jihwan Choe, Jisung Yoo, Kwanghoon Sohn Sensors, vol. 20, pp. 5102, Jun. 2020 (Impact factor: 3.275) Paper
	Multi-task Self-supervised Visual Representation Learning for Monocular Road Segmentation Jaehoon Cho, Youngjung Kim, Hyungjoo Jung, Changjae Oh, Jaesung Youn, Kwanghoon Sohn Sohn IEEE International Conference on Multimedia and Expo (ICME) 2018 (Oral Presentation) Paper | Data

Technical Report

DIML/CVL RGB-D Dataset: 2M RGB-D Images of Natural Indoor and Outdoor Scenes Report

Invited Talks

NAVER LABS, “A Study on Outdoor Scene Understanding in the Dynamic Outdoor Environment”, 2021.
42dot, “Deep Neural Network for Single Image De-raining using Real-world Time-lapse Data”, 2021.