Ming Qian

I am a second year PhD student in Wuhan University, co-supervised by Prof. Gui-Song Xia and Nan Xue.

Email  /  Google Scholar  /  Github

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2023-07      Sat2Density is accepted by ICCV 2023.

2020-08      BGGAN is accepted by ECCVW 2020.

2020-07      We win Winner Award at the ECCV AIM 2020 Challenge on Rendering Realistic Bokeh with Congyu and Jiamin.


I'm interested in computer vision and image processing. Much of my research is about inferring the physical world and camera (shape, motion, color, light, bokeh, etc) from images.

Sat2Density: Faithful Density Learning from Satellite-Ground Image Pairs
Ming Qian, Jincheng Xiong, Gui-Song Xia, Nan Xue,
ICCV, 2023
project page / code / arXiv

Sat2Density focuses on the geometric nature of generating high-quality ground street videos conditioned on satellite images learning from collections of satellite-ground image pairs.

Depth and DOF Cues Make Better Defocus Blur Detection
Yuxin Jin*, Ming Qian*, Jincheng Xiong, Nan Xue, Gui-Song Xia
ICME, 2023
arXiv / Code / Paper

D-DFFNet considers the physical mechanism of defocus blur and successfully distinguishes homogeneous regions. In addition, we propose a larger benchmark EBD that includes more DOF cases. The results of detection on multiple public test sets look great.

BGGAN: Bokeh-Glass Generative Adversarial Network for Rendering Realistic Bokeh
Ming Qian, Congyu Qiao, Jiamin Lin, Zhenyu Guo, Chenghua Li, Cong Leng, Jian Cheng
ECCVW, 2020
arXiv / Code

BGGAN lets us synthesis bokeh effect images from bokeh-less images end to end. Rank 1st in eccv AIM 2020 challenge .

Design and source code from Jon Barron's website