Internship - Characterizing And Optimizing Multi-View Vision Systems H/F INRIA

Internship - Characterizing and Optimizing Multi-View Vision Systems
Le descriptif de l'offre ci-dessous est en Anglais
Type de contrat : Stage

Niveau de diplôme exigé : Bac +4 ou équivalent

Fonction : Stagiaire de la recherche

A propos du centre ou de la direction fonctionnelle

The Inria Grenoble research center groups together almost 600 people in 26 research teams and 8 research support departments.

Staff is present on three campuses in Grenoble, in close collaboration with other research and higher education institutions (University Grenoble Alpes, CNRS, CEA, INRAE, ...), but also with key economic players in the area.

Inria Grenoble is active in the fields of high-performance computing, verification and embedded systems, modeling of the environment at multiple levels, and data science and artificial intelligence. The center is a top-level scientific institute with an extensive network of international collaborations in Europe and the rest of the world.

Contexte et atouts du poste

Context

Multi-view reconstruction aims to recover the 3D geometry of a scene from images captured by multiple cameras (e.g., ). By combining observations of the same scene points from different viewpoints, multi-view methods can estimate depth and reconstruct detailed 3D surfaces. Reconstruction quality strongly depends on the camera configuration, including camera positions, orientations, focal lengths, fields of view, and image resolutions. Together, these parameters determine which parts of the scene are visible, how accurately they are observed, and how effectively their 3D geometry can be recovered. However, a general framework for quantitatively characterizing the ability of a multi-camera system to reconstruct a given 3D scene is still lacking.
Recent work on camera placement and next-best-view selection has primarily focused on coverage and observation quality. In particular, investigates camera placement using learned observation fields, while proposes selecting informative views by prioritizing geometry that is insufficiently covered by existing cameras. Although these approaches provide useful tools for optimizing camera configurations, they do not establish a general framework for quantitatively assessing the reconstruction capabilities of a multi-view vision system.

[1] Toussaint et al., CVPR 2025
[2] Cao et al., IEEE Robotics and Automation Letters 2024
[3] Chen et al., CVPR 2026
[4]Rahimian & Kearney, IEEE TVCG 2017

Team and Supervision

The internship will be carried out within the team at INRIA Grenoble Rhône-Alpes. The student will be supervised by and .

Mission confiée

Internship

The goal of this internship is to develop a general framework forcharacterizing the observation capabilities of multi-view systems.

Metrics that will be investigated include:

- the number of pixels observing each voxel, leading to a3D observation density;
- the number of pixels observing each surface element, leading to asurface observation density;
- multi-view redundancy and viewing-direction distributions;
- potentially, metrics related to geometric reconstruction quality.

Principales activités

Part of the internship will be the development of areusable libraryimplementing these metrics together with interactive 2D/3D visualization tools for analyzing camera configurations and identifying poorly observed regions.

A more exploratory objective will be to use these metrics forcamera-system optimization. Given a target scene and an objective, the system could optimize camera positions, orientations, focal lengths, and resolutions to maximize coverage, observation density, or another task-specific criterion. This connects the project to recent 3]on camera placement and active view selection.

The project combinesmulti-view geometry, 3D vision, optimization, and software development, with the possibility of pursuing research directions depending on the results.

Compétences

Profile

We are looking for a highly motivated Master student M2 in Computer Science, Computer Vision, Applied Mathematics, Robotics, Computer Graphics, or a related field. Good programming skills in Python and/or C++ are required. Background in computer vision, computer graphics, robotics, or 3D geometry would be appreciated.

Avantages

- Subsidized meals
- Partial reimbursement of public transport costs
- Possibility of teleworking (90 days / year) and flexible organization of working hours
- Social, cultural and sports events and activities

Rémunération

Minimum legal gratification