Internship - Characterizing and Optimizing Multi-View Vision Systems

1000scholars; Montbonnot-Saint-Martin; Remote; EUR 10,000 - 15,000; Full time

Job summary

INRIA Montbonnot seeks a highly motivated Master student (M2) to work on characterizing and optimizing multi-view vision systems. The internship is carried out within the Kinetix team, supervised by Edmond Boyer and Julien Pansiot, focusing on developing a framework to assess observation capabilities of multi-view setups.

You will implement metrics such as pixel coverage per voxel and per surface element, build interactive visualization tools, and explore camera-system optimization to maximize

Qualifications

• Strong programming skills in Python and/or C++.
• Background in computer vision, computer graphics, robotics, or 3D geometry.

Responsibilities

• Develop a reusable library implementing observation metrics.
• Create interactive 2D/3D visualization tools for camera configurations.
• Investigate camera-system optimization to maximize coverage and observation density.
• Collaborate with the Kinetix team under supervision by senior researchers.

Skills

Python

C++

Computer Vision

3D geometry

Education

Master student M2 in Computer Science or related field

Job description

# Internship - Characterizing and Optimizing Multi-View Vision SystemsInria MontbonnotMontbonnot, FranceResearcher / ScientistGeometryRoboticsComputer Vision29 days left## About the position**Context****Context**Multi-view reconstruction aims to recover the 3D geometry of a scene from images captured by multiple cameras (e.g., [1] ). 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, [2] investigates camera placement using learned observation fields, while [3] 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] ProbeSDF Toussaint et al., CVPR 2025 [2] Neural Observation Field Guided Hybrid Optimization of Camera Placement Cao et al., IEEE Robotics and Automation Letters 2024 [3] Coverage Optimization for Camera View Selection Chen et al., CVPR 2026 [4] Optimal Camera Placement for Motion Capture Systems Rahimian & Kearney, IEEE TVCG 2017 Team and Supervision The internship will be carried out within the Kinetix team at INRIA Grenoble Rhône-Alpes. The student will be supervised by Edmond Boyer and Julien Pansiot .**Assignment**Internship The goal of this internship is to develop a general framework for characterizing the observation capabilities of multi-view systems. Metrics that will be investigated include: the number of pixels observing each voxel, leading to a 3D observation density; the number of pixels observing each surface element, leading to a surface observation density; multi-view redundancy and viewing-direction distributions; potentially, metrics related to geometric reconstruction quality.**Main activities**Part of the internship will be the development of a reusable library implementing 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 for camera-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 work [ 3]on camera placement and active view selection. The project combines multi-view geometry, 3D vision, optimization, and software development, with the possibility of pursuing research directions depending on the results.**Skills****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.This listing was collected from a public source and is reproduced here forinformation only. Always confirm the details on the original posting before applying. View the original postingApply on the source siteTakes you to Inria Montbonnot.Deadline: 04 Dec 2026Starts: 01 Feb 2027Duration: 5 monthsPosted: 05 Nov 2026Institution: Inria MontbonnotLocation: Montbonnot, France