Schematic Surface Reconstruction
Venue
IEEE Conference on Computer Vision and Pattern Recognition, IEEE (2012)
Publication Year
2012
Authors
Changchang Wu, Sameer Agarwal, Brian Curless, Steven M. Seitz
BibTeX
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.
