Fast, Approximate Piecewise-Planar Modeling Based on Sparse Structure-from-Motion and Superpixels

András Bódis-Szomorú
Hayko Riemenschneider
Luc Van Gool

IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Columbus, Ohio, USA, 24-27 June 2014

Abstract

State-of-the-art Multi-View Stereo (MVS) algorithms deliver dense depth maps or complex meshes with very high detail, and redundancy over regular surfaces. In turn, our interest lies in an approximate, but light-weight method that is better to consider for large-scale applications, such as urban scene reconstruction from ground-based images. We present a novel approach for producing dense reconstructions from multiple images and from the underlying sparse Structure-from-Motion (SfM) data in an efficient way. To overcome the problem of SfM sparsity and textureless areas, we assume piecewise planarity of man-made scenes and exploit both sparse visibility and a fast over-segmentation of the images. Reconstruction is formulated as an energy-driven, multi-view plane assignment problem, which we solve jointly over superpixels from all views while avoiding expensive photoconsistency computations. The resulting planar primitives – defined by detailed superpixel boundaries – are computed in about 10 seconds per image.

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Paper (8 pages)

Poster

BibTeX

@inproceedings{BodisCVPR2014,
author =”Andr{\’a}s B{\’o}dis-Szomor{\’u} and Hayko Riemenschneider and Luc Van Gool”,
title =”Fast, Approximate Piecewise-Planar Modeling Based on Sparse Structure-from-Motion and Superpixels”,
booktitle = “IEEE Conference on Computer Vision and Pattern Recognition (CVPR)”,
year =”2014″,
}