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Patchwork Stereo: Scalable, Structure-Aware 3D Reconstruction in Man-Made Environments

  • Université Paris-Est

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Résumé

In this paper, we address the problem of Multi-View Stereo (MVS) reconstruction of highly regular man-made scenes from calibrated, wide-baseline views and a sparse Structure-from-Motion (SfM) point cloud. We introduce a novel patch-based formulation via energy minimization which combines top-down segmentation hypotheses using appearance and vanishing line detections, as well as an arrangement of creased planar structures which are extracted automatically through a robust analysis of available SfM points and image features. The method produces a compact piecewise-planar depth map and a mesh which are aligned with the scene's structure. Experiments show that our approach not only reaches similar levels of accuracy w.r.t state-of-The-Art pixel-based methods while using much fewer images, but also produces a much more compact, structure-Aware mesh in a considerably shorter runtime by several of orders of magnitude.

langue originaleAnglais
titreProceedings - 2017 IEEE Winter Conference on Applications of Computer Vision, WACV 2017
EditeurInstitute of Electrical and Electronics Engineers Inc.
Pages292-301
Nombre de pages10
ISBN (Electronique)9781509048229
Les DOIs
étatPublié - 11 mai 2017
Modification externeOui
Evénement17th IEEE Winter Conference on Applications of Computer Vision, WACV 2017 - Santa Rosa, États-Unis
Durée: 24 mars 201731 mars 2017

Série de publications

NomProceedings - 2017 IEEE Winter Conference on Applications of Computer Vision, WACV 2017

Une conférence

Une conférence17th IEEE Winter Conference on Applications of Computer Vision, WACV 2017
Pays/TerritoireÉtats-Unis
La villeSanta Rosa
période24/03/1731/03/17

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