ML Anthology

Schematic Surface Reconstruction

Changchang Wu, Sameer Agarwal, Brian Curless, Steven M. Seitz
CVPR 2012 pp. 1498-1505
doi:10.1109/CVPR.2012.6247839 /cvpr/2012/wu2012cvpr-schematic/

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.

PDF CVPR Semantic Scholar

Cite

Text

Wu et al. "Schematic Surface Reconstruction." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2012. doi:10.1109/CVPR.2012.6247839

Markdown

[Wu et al. "Schematic Surface Reconstruction." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2012.](https://mlanthology.org/cvpr/2012/wu2012cvpr-schematic/) doi:10.1109/CVPR.2012.6247839

BibTeX

@inproceedings{wu2012cvpr-schematic,
  title     = {{Schematic Surface Reconstruction}},
  author    = {Wu, Changchang and Agarwal, Sameer and Curless, Brian and Seitz, Steven M.},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2012},
  pages     = {1498-1505},
  doi       = {10.1109/CVPR.2012.6247839},
  url       = {https://mlanthology.org/cvpr/2012/wu2012cvpr-schematic/}
}