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Research scientist at Valeo.ai
Machine learning and point cloud expert

Publications highlights

SLidR: Image-to-Lidar Self-Supervised Distillation for Autonomous Driving Data
Accepted at Computer Vision and Pattern Recognition (CVPR) 2022
POCO: Point Convolution for Surface Reconstruction
Accepted at Computer Vision and Pattern Recognition (CVPR) 2022

Here is a more complete list of publications

Research

Machine Learning

Machine learning

Machine Learning is a transverse field that aims at extracting information from data in order to make decisions and predictions. I currently work to develop new architectures ( ShaResNet ), apply them various application fields from classification to cartography to 3D scene understanding to space wearther prediction.

Geometry

Geometry

Computational Geometry and reconstruction was the core area of my thesis. Understanding 3D scenes, and reconstructing abstracted surfaces is a major interest to me. I work on outdoor scenes ( Semantic3D ) as well as indoor data for robotics ( SUNRGB-D ).

Thesis

at LIGM (ENPC), under the supervision of Renaud Marlet.

Subject: “Automatic reconstruction of 3D building information models”.

Previous research at ONERA

Earth Observation

Earth Observation

Earth Observation data is now massively available through satellite programs like Copernicus. This allows the emergence of new algorithms and techniques to cartography, analysis or restoration of multiple sensors active (SAR) or passive (optical) images. I am particularly interested in dealing with partially annoted data to create robust machine learning techniques abble to diggest multi-temporal data.

Some projects I have worked on while being a reasearcher at ONERA

DeLTA, deep machine learning for aerospace applications

Medusa for Big Data in Earth Observation

Inachus for Urban Search and Rescue