Informations générales
Entité de rattachement
Le CEA est un acteur majeur de la recherche, au service des citoyens, de l'économie et de l'Etat.
Il apporte des solutions concrètes à leurs besoins dans quatre domaines principaux : transition énergétique, transition numérique, technologies pour la médecine du futur, défense et sécurité sur un socle de recherche fondamentale. Le CEA s'engage depuis plus de 75 ans au service de la souveraineté scientifique, technologique et industrielle de la France et de l'Europe pour un présent et un avenir mieux maîtrisés et plus sûrs.
Implanté au cœur des territoires équipés de très grandes infrastructures de recherche, le CEA dispose d'un large éventail de partenaires académiques et industriels en France, en Europe et à l'international.
Les 20 000 collaboratrices et collaborateurs du CEA partagent trois valeurs fondamentales :
• La conscience des responsabilités
• La coopération
• La curiosité
Référence
2026-41959
Description de l'unité
Based in Saclay (Essonne), the LIST is one of the two institutes of CEA Tech, the Technological Research Division of the CEA. Dedicated to intelligent digital systems, its mission is to carry out technological developments of excellence on behalf of industrial partners, in order to create value.
Within the LIST, the Laboratory of Vision and Learning for Scene Analysis (LVA) conducts its research in the field of computer vision and artificial intelligence for the perception of intelligent and autonomous systems. The laboratory's research themes include visual recognition, behavior and activity analysis, large-scale automatic annotation, and perception and decision models.
Description du poste
Domaine
Mathématiques, information scientifique, logiciel
Contrat
Stage
Intitulé de l'offre
Stage 4D Gaussian splatting for autonomous driving-Saclay- H/F
Sujet de stage
4D Gaussian splatting for autonomous driving
Durée du contrat (en mois)
6 mois
Description de l'offre
As an intern at the CEA, you will have the opportunity to work in a world-renowned research environment. Our teams consist of passionate and dedicated experts, providing an environment conducive to learning and collaboration. You will have access to state-of-the-art equipment and top-tier research resources to carry out your assignments. The work performed may potentially lead to a scientific publication.
Context
Please read the attached file for proper formatting and illustration.
Autonomous driving requires long term understanding of the 3D driving environment in order to correctly perceive both its coarse general structure (HD map, road topology) and its fine constituting elements (vehicles, pedestriand, traffic signs ...) .
In this internship, we propose to generate gaussians from multimodal inputs (camera, LIDAR, RADAR) . These gaussians will then be used as an input that contains both global and fine-grained context necessary for 3D perception in the scene. The candidate will then adapt 3D perception algorithms to this input modality in order to achieve SOTA performance on perception tasks such as 3D detection and HD map estimation.
What do we expect from you?
To achieve these objectives, the intern will be expected to:
- Review the state of the art on gaussian splatting reconstruction and 3D perception
- Design, develop and evaluate a novel deep learning pipeline for perception in autonomous driving scenes
- Contribute to research reports and potential publications
References:
[1] LU, Yiren, YE, Xin, YAMAN, Burhaneddin, et al. Reconstruction Matters: Learning Geometry-Aligned BEV Representation through 3D Gaussian Splatting. arXiv preprint arXiv:2603.19193, 2026.
[2] CHABOT, Florian, GRANGER, Nicolas, et LAPOUGE, Guillaume. Gaussianbev: 3d gaussian representation meets perception models for bev segmentation. In : 2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). IEEE, 2025. p. 2250-2259.
[3] KERBL, Bernhard, KOPANAS, Georgios, LEIMKÜHLER, Thomas, et al. 3d gaussian splatting for real-time radiance field rendering. ACM Trans. Graph., 2023, vol. 42, no 4, p. 139:1-139:14.
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Moyens / Méthodes / Logiciels
PYTHON, Pytorch, CUDA, proprietary software
Profil du candidat
Profile :
Students in their 4th or 5th year of studies (M1, M2 or gap year)
Computer vision skills
Machine learning skills (deep learning, perception models, generative AI…)
Python proficiency in a deep learning framework (especially PyTorch or TensorFlow)
Strong interest for 3D
Localisation du poste
Site
Saclay
Localisation du poste
France, Ile-de-France, Essonne (91)
Ville
Saclay
Critères candidat
Diplôme préparé
Bac+5 - Diplôme École d'ingénieurs
Formation recommandée
Master 2 ou équivalent
Demandeur
Disponibilité du poste
01/01/2027
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