I am a second-year PhD student (expected graduation on 09/2023) at the École des Ponts in the computer vision team IMAGINE (LIGM, École des Ponts, Univ Gustave Eiffel, CNRS) and at the IGN—the French Mapping Agency—in the Spatio-Temporal Structures for Spatial Analysis team of the LASTIG (LASTIG, Univ Gustave Eiffel, IGN/ENSG), advised by Mathieu Aubry and Loïc Landrieu. I am interested in optimisation and machine learning for 3D data, with a focus on unsupervised learning, interpretability, and real-time applications.


  • Mathieu Aubry (LIGM, Ecole des Ponts, Univ Gustave Eiffel, CNRS, France)
  • Loïc Landrieu (Univ Paris-Est, IGN-ENSG, LaSTIG, STRUDEL, Saint-Mandé, France)

🎉 News 🎉

📜 Publications 📜

Online Segmentation of LiDAR Sequences: Dataset and Algorithm, arXiv, 2022
Romain Loiseau, Mathieu Aubry, Loïc Landrieu
Paper | Webpage | Helix4D Implementation | Download HelixNet | HelixNet Toolbox

First, we introduce HelixNet, a 10-billion point dataset with fine-grained timestamps and sensor rotation information. Second, we propose Helix4D, a compact and efficient spatio-temporal transformer architecture specifically designed for rotating LiDAR sequences that reaches accuracy on par with the best segmentation algorithms with a reduction of over 5× in terms of latency and 50× in model size.

A Model You Can Hear: Audio Classification with Playable Prototypes, CVPR Sight and Sound Workshop, 2022
Romain Loiseau, Baptiste Bouvier, Yann Teytaut, Elliot Vincent, Mathieu Aubry, Loïc Landrieu
Paper to come | Webpage | Code to come

We propose an audio identification model based on learnable spectral prototypes. Our model can be trained with or without supervision and reaches state-of-the-art results for speaker and instrument identification, while remaining easily interpretable.

Representing Shape Collections with Alignment-Aware Linear Models, 3DV, 2021
Romain Loiseau, Tom Monnier, Mathieu Aubry, Loïc Landrieu
Paper | Webpage | Code | Slides | Long video | Short video

We characterize 3D shapes as affine transformations of linear families learned without supervision, and showcase its advantages on large shape collections.

This work is an extension of the Deep Transformation-Invariant Clustering framework from Tom Monnier et al. for 3D tasks such as clustering and few-shot segmentation.

💼 Short Resume 💼

2020-now PhD student on "Semantic segmentation of dynamic 3D point clouds" supervised by Mathieu Aubry and Loic Landrieu.
Summer 2019 Research intern on generative adversarial network for MRI spine labelling at GE Healthcare supervised by Mathieu Aubry.
2018-2019 Mathematics, Vision and Learning (MVA) master of the ENS-Cachan & engineering program of Ecole des Ponts ParisTech.
Summer 2018 Research intern on the semantic segmentation of 3D point clouds using deep learning at Bentley Systems with Renaud Keriven.
2015-2018 Master in Computer science and biology at the Ecole polytechnique.

You can find my detailled resume here.