Internship - Characterizing and Optimizing Multi-View Vision Systems

id="offer-description" class="ecl-u-type-heading-2 ecl-u-mt-none ecl-u-mb-l">Offer Description . 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 Opti