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

András Bódis-Szomorú, Hayko Riemenschneider and Luc Van Gool
IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Columbus, OH, USA, 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.


Link to publisher's page
@InProceedings{eth_biwi_01141,
  author = {András Bódis-Szomorú 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},
  month = {June},
  pages = {469-476},
  keywords = {reconstruction, superpixels, structure-from-motion}
}