Internship 3D Reconstruction in the Dark: Radiance Fields for Unlit Environments-Saclay-H/F

Description du poste:

Description du poste

Domaine

Sciences pour l'ingénieur

Contrat

Stage

Intitulé de l'offre

Internship 3D Reconstruction in the Dark: Radiance Fields for Unlit Environments-Saclay-H/F

Sujet de stage

3D reconstruction from images is a rapidly evolving field with major applications in robotics, augmented reality, archaeology, and industrial inspection. Modern methods, such as Neural Fields (NeRF, Instant NGP) and Gaussian Splatting, enable the creation of highly detailed and realistic 3D scenes. However, these approaches often assume well-lit and static environments, which limits their applicability in real-world scenarios where lighting is absent or variable. This internship proposes exploring an innovative approach: integrating a mobile embedded lighting system (e.g., a light source mounted on a robot) to enable 3D reconstruction in unlit or hard-to-access environments.

Durée du contrat (en mois)

6 mois

Description de l'offre

As an intern at the CEA, you will have the opportunity to work within a world-renowned research environment. Our teams consist of passionate and dedicated experts, providing an ideal setting for 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 conducted may potentially lead to a scientific publication.

What do we expect from you?
The intern will be responsible for:
*State of the Art Review: Analyzing existing 3D reconstruction methods (NeRF, Gaussian Splatting) and their limitations in unlit environments.
*Prototyping: Implementing a solution that model the light source in the inverse rendering reconstruction pipeline.
*Experimentation: Testing the system in real or simulated scenarios, with different light configurations.
*Optimization: Improving robustness and the reconstruction accuracy (large environment, reflective sufaces)
*Validation: Comparing results with classical methods (with fixed lighting) and evaluating improvements in quality and robustness.

#Cea-List

Moyens / Méthodes / Logiciels

python

Profil du candidat

* Currently enrolled in your final year of a Master's program (M2) or an Engineering School (including gap year students).
* Strong skills in computer vision and machine learning, particularly.
* High proficiency in Python alongside experience using a deep learning framework, with a strong preference for PyTorch.

Localisation du poste

Site

Saclay

Localisation du poste

France, Ile-de-France, Essonne (91)

Ville

Saclay

Critères candidat

Diplôme préparé

Bac+5 - Master 2