Floor-SP: Inverse CAD for Floorplans by Sequential Room-Wise Shortest Path

Jiacheng Chen, Chen Liu, Jiaye Wu, Yasutaka Furukawa; The IEEE International Conference on Computer Vision (ICCV), 2019, pp. 2661-2670

Abstract


This paper proposes a new approach for automated floorplan reconstruction from RGBD scans, a major milestone in indoor mapping research. The approach, dubbed Floor-SP, formulates a novel optimization problem, where room-wise coordinate descent sequentially solves shortest path problems to optimize the floorplan graph structure. The objective function consists of data terms guided by deep neural networks, consistency terms encouraging adjacent rooms to share corners and walls, and the model complexity term. The approach does not require corner/edge primitive extraction unlike most other methods. We have evaluated our system on production-quality RGBD scans of 527 apartments or houses, including many units with non-Manhattan structures. Qualitative and quantitative evaluations demonstrate a significant performance boost over the current state-of-the-art. Please refer to our project website http://jcchen.me/floor-sp/ for code and data.

Related Material


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[bibtex]
@InProceedings{Chen_2019_ICCV,
author = {Chen, Jiacheng and Liu, Chen and Wu, Jiaye and Furukawa, Yasutaka},
title = {Floor-SP: Inverse CAD for Floorplans by Sequential Room-Wise Shortest Path},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {October},
year = {2019}
}