Title :
Schematic surface reconstruction
Author :
Wu, Changchang ; Agarwal, Sameer ; Curless, Brian ; Seitz, Steven M.
Author_Institution :
Univ. of Washington, Seattle, WA, USA
Abstract :
This paper introduces a schematic representation for architectural scenes together with robust algorithms for reconstruction from sparse 3D point cloud data. The schematic models architecture as a network of transport curves, approximating a floorplan, with associated profile curves, together comprising an interconnected set of swept surfaces. The representation is extremely concise, composed of a handful of planar curves, and easily interpretable by humans. The approach also provides a principled mechanism for interpolating a dense surface, and enables filling in holes in the data, by means of a pipeline that employs a global optimization over all parameters. By incorporating a displacement map on top of the schematic surface, it is possible to recover fine details. Experiments show the ability to reconstruct extremely clean and simple models from sparse structure-from-motion point clouds of complex architectural scenes.
Keywords :
edge detection; image reconstruction; complex architectural scenes; dense surface interpolation; floorplan approximation; global optimization; profile curves; schematic models architecture; schematic representation; schematic surface reconstruction; sparse 3D point cloud data reconstruction; sparse structure-from-motion point clouds; transport curves; Image reconstruction; Merging; Noise; Optimization; Robustness; Shape; Surface reconstruction;
Conference_Titel :
Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on
Conference_Location :
Providence, RI
Print_ISBN :
978-1-4673-1226-4
Electronic_ISBN :
1063-6919
DOI :
10.1109/CVPR.2012.6247839