Lam Huynh

Moikka! I am a PhD student at Center for Machine Vision and Signal Analysis, University of Oulu, Finland, where I am co-advised by Prof. Janne Heikkilä and Prof. Esa Rahtu. My work is funded by the Infotech Oulu.

I'm interested in 3D scene understanding, reconstruction and 3D human pose estimation. My research combines 3D computer vision and deep learning.

I am also curious about effective learning and teaching techniques.

Google Scholar
Curriculum vitae
Stack Overflow
Guiding Monocular Depth Estimation Using Depth-Attention Volume
Lam Huynh, Phong Nguyen-Ha, Jiří Matas, Esa Rahtu, Janne Heikkilä
ECCV, 2020
project page | arXiv | code
Structure-from-motion using convolutional neural networks
Master thesis, funded by Cubicasa Oy
Published on Jultika, 2018
Supervisors: Prof. Janne Heikkilä, Dr. Markus Ylimäki

This work introduces a deep-learning-based structure-from-motion pipeline for the dense 3D scene reconstruction problem.

3D human pose estimation using deep neural networks
Summer internship, IMD lab NAIST, Japan. 15th September 2018.

Working on Human pose estimation for Sprint (running) using learning-based approaches. In this work, I collaborated with MSc. Yuma Ouchi, MSc. Taisei Watanabe and Assoc. Prof. Takafumi Taketomi.

Reading notes
Camera poses and coordinates for dummies
Documentation, Oulu, March 2019

This document is for everyone who still confuse with the relative camera poses and the relationship betweeen the camera and world coordinate. How to do the transformation from one camera coordinate to the other camera coordinate. There are many ways to do this wrong, therefore we should clearly understand these terms.

University pedagogy training
Documentation, Oulu, 30th March 2019

Learning diary of the basic of university pedagogy, from which I learned the fundamental of teaching and had lot of interesting discussions. Thanks to Dr. Sonja Lutovac for this amazing course.

The four Rs technique to become a deep learner
Documentation, Oulu, 05th April 2019

A practical scheme to remember and understand deeply what we have learned.


Autumn 2019: Deep learning (Dr. Li Liu, Dr. Jie Chen)
Autumn 2020: Deep learning (Dr. Li Liu)
I work as a Teaching assisstant alongside with MSc. Zhuo Su

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