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

Andras Bodis-Szomoru, Hayko Riemenschneider, Luc Van Gool; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014, pp. 469-476

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.

Related Material


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[bibtex]
@InProceedings{Bodis-Szomoru_2014_CVPR,
author = {Bodis-Szomoru, Andras and Riemenschneider, Hayko and Van Gool, Luc},
title = {Fast, Approximate Piecewise-Planar Modeling Based on Sparse Structure-from-Motion and Superpixels},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2014}
}