Schematic Surface Reconstruction

Changchang Wu
Brian Curless
Steven M. Seitz
IEEE Conference on Computer Vision and Pattern Recognition, IEEE (2012)
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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.

Research Areas