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Jean-Emmanuel Deschaud

Jean-Emmanuel Deschaud

Research Director

Center · CAOR

Discipline(s)
Signal, Image, Automatic Control, Robotics and Industrial Engineering
Topic(s)
Robotics

Awards & distinctions

  • 2025 Best Paper Award à WACV 2025 pour l'article "RayGauss: Volumetric Gaussian-Based Ray Casting for Photorealistic Novel View Synthesis".
  • 2022 Finalist for the "ICRA 2022 Outstanding Paper" award for the paper "CT-ICP: Real-time Elastic LiDAR Odometry with Loop Closure"; 3 papers were selected out of 1,498 submitted to the conference.
  • 2021 Best Paper Award du Working Group on Graphics and Cultural Heritage 2021 pour l'article “Riedone3D: a celtic coins dataset for registration and fine-grained clustering”

Team

CAOR

Biography

Jean-Emmanuel Deschaud is a research director at the Centre for Robotics at Mines Paris – PSL. A graduate of Mines Paris and holder of a research habilitation from Sorbonne University, he has been conducting research in 3D perception and modeling for nearly twenty years. His work focuses notably on LiDAR point cloud processing, localization and mapping for robotics and autonomous vehicles, semantic segmentation, and generalization of 3D models. His research also extends to image-LiDAR registration, 3D reconstruction, and novel view synthesis. He develops robust and generalizable methods, suited to real, dynamic, and complex environments.

Publication(s)

Teaching

Point clouds and 3D modeling

2014 – en cours Instructor in charge

This course provides an overview of the concepts and techniques for acquiring, processing, and visualizing 3D point clouds, as well as their mathematical and algorithmic foundations.

PhD supervision

  • 2025 3D reconstruction, high-quality rendering, and stabilization from high-resolution cameras BEN MABROUK Souheib
  • 2025 Point cloud and image registration via differentiable rendering INSALACO Ugo
  • 2025 3D Generative Models with Geometric Constraints OLECH Alexandre
  • 2025 Digitization of objects using radiance fields: acquisition, rendering, and evaluation with relighting on different types of displays LEBSAIRA Wassil
  • 2022 Odometry and local elevation maps for leg exoskeletons with a limited field-of-view depth sensor ELNECAVE XAVIER Fabio
  • 2022 Deep neural networks for semantic segmentation of 3D point clouds adapted to embedded architectures for autonomous mobile and robotic systems ABOU HAIDAR Samir
  • 2022 Realistic differentiable rendering of point clouds for interactive visualization of real environments. Applications to cultural heritage for the study of archaeological objects and virtual visits of monuments. BLANC Hugo
  • 2021 Generalization of domain and 3D detection of unknown objects from LiDAR data SOUM-FONTEZ Louis
  • 2020 Exploration of LiDAR odometry through classical, deep learning, and inertial perspectives DELLENBACH Pierre
  • 2020 Generalization of domain for semantic segmentation of LiDAR data for autonomous vehicles SANCHEZ Jules
  • 2019 Comparison of patterns on 3D point clouds and application to Celtic coins and artifacts HORACHE Sofiane
  • 2019 Urban scene modeling from 3D point clouds and massive LiDAR simulation for autonomous vehicles RICHA Jean Pierre
  • 2018 3D urban scene understanding through LiDAR, color, and hyperspectral data analysis DUQUE David
  • 2016 Learning new representations for the semantic enrichment of 3D point clouds THOMAS Hugues
  • 2015 On-the-Fly Semantization of 3D Point Clouds Acquired by Embedded Systems ROYNARD Xavier