Summary¶
As a PhD candidate at IGN—the French Mapping Agency 🗺—in the machine
learning STRUDEL team (LASTIG lab, IGN-ENSG, Univ. Gustave Eiffel),
and in the CSAI team at ENGIE Lab CRIGEN, I design deep learning methods for multimodal,
multi-task learning on large-scale point clouds. Specifically, my recent research involves
computer vision on 3D point clouds and 2D images in the wild.
You like point clouds ☁ ? You like images 📸 ? You like me 😊.
Supervisors¶
- Loïc Landrieu (STRUDEL, LASTIG, Univ. Gustave Eiffel, IGN-ENSG, Saint-Mandé, France)
- Bruno Vallet (ACTE, LASTIG, Univ. Gustave Eiffel, IGN-ENSG, Saint-Mandé, France)
📃 Publications¶
🖼 Poster | 🎤 OralDamien Robert, Hugo Raguet, Loïc Landrieu
In Review
SuperCluster proposes a framework for efficient panoptic segmentation of large-scale
point clouds. We formulate the panoptic task as a graph clustering problem. We train
a model to predict desirable node and edge attributes to be used as input for a
downstream graph clustering algorithm. This allows for training a model with only
local supervision, without the need for non-maximum suppression, instance matching,
and without any prerequisite on the number of objects in the scene.
SuperCluster achieves new SOTA panoptic segmentation on indoor datasets S3DIS Area 5
(46.3 PQ (+4.0)) and ScanNetV2 (54.5 PQ (+21.0)), as well as outdoor datasets
KITTI-360 (48.1 PQ) and DALES (53.9 PQ).
🦋 209k param. | ⚡ Train S3DIS F5 in 4h | 💾 20M-point inference on 1 GPU
Damien Robert, Hugo Raguet, Loïc Landrieu
Paper | Webpage | Code
ICCV 2023
SPT is a superpoint-based transformer 🤖 architecture that efficiently ⚡
performs semantic segmentation on large-scale 3D scenes. This method includes a fast
algorithm that partitions 🧩 point clouds into a hierarchical superpoint structure,
as well as a self-attention mechanism to exploit the relationships between
superpoints at multiple scales.
We reach SOTA on S3DIS 6-Fold (76.0 mIoU),
KITTI-360 Val (63.5 mIoU), and DALES
(79.6 mIoU)n with:
🦋 212k param. | ⚡ Train S3DIS F5 in 3h | ⌚ SPG preprocessing ÷7
Damien Robert, Bruno Vallet, Loïc Landrieu
Paper | Webpage | Code | Video
CVPR 2022 Oral 🎤 and Best Paper finalist 🎉
An end-to-end multi-view aggregation method for 3D semantic segmentation from images and point clouds. We reach SOTA on S3DIS and KITTI-360 without requiring point cloud colorization, meshing, or depth sensors: just point clouds ☁, images 📸, and their poses.
📑 Short Resume¶
- 2020 - Now PhD student on "Multimodal, multi-task learning on large-scale 3D point clouds" supervised by Loïc Landrieu and Bruno Vallet
- 2022 International Computer Vision Summer School
- 2017 CNRS AI Fall School IA² , Multi-disciplinary course for AI students and researchers
- 2011 - 2015 Master of Science at Ecole Centrale de Lyon
🖼🎤 Talks & Presentations¶
🖼 Poster | 🎤 Oral- 🎤 09/2023 Presenting our work Superpoint Transformer to the ETH Zürich, Photogrammetry and Remote Sensing lab
- 🎤 07/2023 Our work Superpoint Transformer was accepted at ICCV 2023 🎉
- 🎤 06/2023 Presenting our work Superpoint Transformer to the ENGIE Lab CRIGEN
- 🎤 05/2023 Presenting our work Superpoint Transformer to the University of Zürich, EcoVision lab
- 🎤 05/2023 Presenting our work Superpoint Transformer to the Samp R&D lab
- 🎤 05/2023 Presenting our work Superpoint Transformer to the Valeo.ai lab
- 🎤 12/2022 Presenting Self-Supervised Learning for Computer Vision to the LASTIG lab
- 🎤 11/2022 Presenting IGN's research on Large-Scale 2D and 3D Learning to the BKG (Federal Agency for Cartography and Geodesy)
- 🖼 07/2022 Presenting our work DeepViewAgg at ICVSS
- 🎤 06/2022 Presenting our work DeepViewAgg at CVPR 2022 (Best Paper finalist 🎉)
- 🎤 06/2022 Had the honor to be interviewed for the CV News Best of CVPR issue
- 🎤 06/2022 Presenting our work DeepViewAgg to the Ecole des Ponts, IMAGINE lab
- 🖼 06/2022 Presenting our work DeepViewAgg at ISPRS 2022
- 🎤 05/2022 Presenting our work DeepViewAgg to the Ecole Polytechnique, LIX lab
- 🎤 04/2022 Presenting our work DeepViewAgg at the AI4GEO project seminar
- 🖼 03/2022 Presenting our work DeepViewAgg at the IGN-ENSG Research Days
- 🎤 01/2022 Presenting our work DeepViewAgg at the Information, Signal, Image and Vision research group seminar
- 🖼 05/2021 Presenting our work DeepViewAgg at the IGN-ENSG Research Days
📚 Teaching¶
- 01/2023 Deep Learning for Remote Sensing at ENSG (Course Instructor, M2, 13 hours)
- 06/2022 3D Deep Learning for Remote Sensing at ISPRS 2022 (Tutorial Instructor, 1 day)
- 05/2022 3D Deep Learning, Torch-Points3D & DeepViewAgg at ENGIE Lab CRIGEN (Tutorial Instructor, 1 day)
- 01/2022 Deep Learning for Remote Sensing at ENSG (Course Instructor, M2, 9 hours)
- 11/2020 Deep Learning for Computer Vision at Ecole Polytechnique (Teaching Assistant, M1, 12 hours)
🏠 Affiliations¶
- CSAI, ENGIE Lab CRIGEN
- Univ. Gustave Eiffel, IGN-ENSG, LASTIG, STRUDEL