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 🎉¶
- 06 / 2022 : Presenting our algorithm for online segmentation of LiDAR sequences at CVPR's Transformer for Vision workshop!
- 06 / 2022 : Presenting our hearable model for audio classification at CVPR's Sight and Sound workshop!
- 06 / 2022 : Invited at ISPRS Congress to present our dataset and algorithm for online segmentation of LiDAR sequences!
- 05 / 2022 : Presenting our hearable model for audio classification at the Information, Signal, Image and Vision research group!
- 10 / 2021 : Presenting our work on representing 3D shapes at ICCV's Learning 3D Representations for Shape and Appearance workshop!
- 10 / 2021 : Our work on representing 3D shapes has been accepted at 3DV 2021!
- 09 / 2020 : Starting PhD under the supervision of Mathieu Aubry and Loïc Landrieu
📜 Publications 📜¶
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.
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.
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.