TY - GEN
T1 - Patchwork Stereo
T2 - 17th IEEE Winter Conference on Applications of Computer Vision, WACV 2017
AU - Bourki, Amine
AU - De La Gorce, Martin
AU - Marlet, Renaud
AU - Komodakis, Nikos
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/5/11
Y1 - 2017/5/11
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/85020223731
U2 - 10.1109/WACV.2017.39
DO - 10.1109/WACV.2017.39
M3 - Conference contribution
AN - SCOPUS:85020223731
T3 - Proceedings - 2017 IEEE Winter Conference on Applications of Computer Vision, WACV 2017
SP - 292
EP - 301
BT - Proceedings - 2017 IEEE Winter Conference on Applications of Computer Vision, WACV 2017
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 24 March 2017 through 31 March 2017
ER -