Mohammad Mahdi Johari

Hello! I am Mohammad Mahdi Johari, currently a Ph.D. student at EPFL, and Research Assistant in Machine Learning Group at Idiap Research Institute under the joint supervision of Prof. François Fleuret and Prof. Daniel Gatica-Perez.

As a part of my PhD study, I did an internship at Apple focusing on fast and efficient 3D representation for human faces.

Prior to joining Idiap, I received my Master degree in Electrical Engineering from Sharif University of Technology. Thereafter, I continued my research as Research Assistant in Computer Vision Lab at Sharif University. I also worked as a Machine Learning Engineer at Cafe Bazaar.

Email / CV / Google Scholar / Twitter / LinkedIn

Research

I am interested in Machine Learning, Computer Vision, and 3D Computer Vision.

The goal of my Ph.D. is to improve the performance of 3D reconstruction and novel view synthesis models that exploit depth information from RGBD sensors.

Publications

	ESLAM: Efficient Dense SLAM System Based on Hybrid Representation of Signed Distance Fields M. M. Johari, C. Carta, F. Fleuret CVPR (Highlight), 2023 Paper / Code / Project Page ESLAM is an efficient implicit neural representation method for Simultaneous Localization and Mapping (SLAM).
	GeoNeRF: Generalizing NeRF With Geometry Priors M. M. Johari, Y. Lepoittevin, F. Fleuret CVPR , 2022 Paper / Code / Project Page GeoNeRF is a generalizable photorealistic novel view synthesis method based on neural radiance fields.
	DepthInSpace: Exploitation and Fusion of Multiple Video Frames for Structured-Light Depth Estimation M. M. Johari, C. Carta, F. Fleuret ICCV, 2021 Paper / Code / Project Page DepthInSpace is a self-supervised deep-learning method for depth estimation using a structured-light camera.
	Context-Aware Colorization of Gray-Scale Images Utilizing a Cycle-Consistent Generative Adversarial Network Architecture M. M. Johari, H. Behroozi Neurocomputing, 2020 Paper We propose a two-stage architecture for image colorization. The first stage generates an initial colored image and selects one of the specialist networks in the second stage which improves the quality of the colored image.
	Gray-Scale Image Colorization Using Cycle-Consistent Generative Adversarial Networks with Residual Structure Enhancer M. M. Johari, H. Behroozi ICASSP, 2020 Paper Proposing a cycle-consistent colorization model based on Generative Adversarial Network (GAN).
	Robust Airborne Target Recognition Based on Recurrence Plot Quantification of Micro-Doppler Radar Signatures M. M. Johari, M. M. Nayebi IRS, 2016 Paper Proposing a method based on Recurrence Plot and Recurrence Quantification Analysis (RQA) to generate robust features against noise.

Thanks to Jon Barron for the template.