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<!DOCTYPE HTML>
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<title>Hugues THOMAS</title>
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<h2>Hugues THOMAS</h2>
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<p>AI/robotics Researcher. Senior Research Engineer @Apple. 3D Point Clouds, Robotics, and Machine Learning.</p>
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<p>I am currently a Senior Research Engineer at Apple. I previously worked as a postdoctoral researcher at the Autonomous Space Robotics Lab (ASRL), University of Toronto Institute for Aerospace Studies.
I received my Ph.D. from Mines ParisTech - Université PSL (France), and prior to that my M.Sc.Eng. from l'École Des Ponts ParisTech (France).</p>
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<p>
My research focuses on deep learning, 3D point clouds and robotics.
I have developed a novel convolutional operator for 3D point clouds, KPConv, and designed deep network architectures
for 3D object classification, object part segmentation and semantic scene parsing.
I have also explored the problems of rotation invariance and equivariance in 3D convolutions, and adapted a rotation-invariant version of KPConv.
Recently I have been interested in the application of deep neural networks to robotics applications.
In particular I study how a robot can use its own past experiences to learn meaningful undertanding of its current environment, via self-supervision.
If you are interested in my projects, feel free to visit the different github pages of my projects, or contact me directly.
</p>
<h1> <br /><br /> </h1>
<header class="major">
<span class="date">Education</span>
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<h3><span class="image left">
<div><img src="images/Logo_Mines.png" alt="" /></div>
</span>
<br />Ph.D. 3D Computer Vision (2016-2019)</h3>
<p><i>Mines Paristech, Université PSL, France</i></p>
<p>
<b>Advisors:</b> François Goulette, Beatriz Marcotegui, Jean-Emmanuel Deschaud. <br />
<a href="https://pastel.archives-ouvertes.fr/tel-02458455/document" target="_blank">
<b>Thesis:</b> Learning New Representations for 3D Point Cloud Semantic Segmentation. <span class="icon2 ai ai-hal ai-hx"> </span>
</a>
<br />
During my Ph.D., I studied handcrafted features and developed a novel multiscale spherical neighborhood definition to compute
features describing 3D points. Using conclusions drawn from handcrafted representations, I designed a point convolution
operator: KPConv. It uses radius neighborhoods and a set of kernel points to play the role of the kernel pixels
in image convolution. This design offers a straightforward deformable extension and very strong performances
(ranked first in many benchmarks). I also studied the learnt representations to compare with handcrafted features.
<br />
<br />
</p>
<h3><span class="image left">
<div><img src="images/Logo_MVA.jpg" alt="" /></div>
</span>
<br />M.Sc. M2 Master MVA (2015-2016)</h3>
<p><i>ENS Paris-Saclay, France</i></p>
<p>
During the last year of my master, I specialized in computer vision and machine learning
in the French M.Sc. MVA (Mathematiques, Vison, Apprentissage).
<br />
<br />
</p>
<h3><span class="image left">
<div><img src="images/Logo_école_des_ponts_paristech.svg" alt="" /></div>
</span>
<br />M.Sc.Eng. Applied Mathematics (2012-2016)</h3>
<p><i>École Des Ponts ParisTech, France</i></p>
<p>
French engineering diploma (equivalent of B.Eng. + M.Sc.Eng.). Majored mathematics and computer sciences.
<br />
<br />
</p>
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<h1> <br /><br /> </h1>
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<div><img src="images/Logo_MVA.jpg" alt="" /></div>
</span><br /></h3>
<p style="font-size:1.0em; line-height:1.9;">
<b>M.Sc. M2 Master MVA: Point clouds and 3D Modeling. (2017-2019)</b> <br />
<i>ENS Paris-Saclay, France</i>
<br />
<br />
</p>
<h3><span class="image left">
<div><img src="images/Logo_Mines.png" alt="" /></div>
</span>
<br /></h3>
<p style="font-size:1.0em; line-height:1.9;">
<b>M.Sc. M2 MAREVA: 3D Modeling Techniques (2016-2019)</b> <br />
<i>Mines Paristech, Université PSL, France</i>
<br />
<br />
</p>
</article>
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