Learning from synthetic models for roof style classification in point clouds

ABSTRACT

Automatic roof style classification using point clouds is useful and can be used as a prior knowledge in various applications, such as the construction of 3D models of real-world buildings. Previous classification approaches usually employ heuristic rules to recognize roof style and are limited to a few roof styles. In this paper, the recognition of roof style is done by a roof style classifier which is trained based on bag of words features extracted from a point cloud. In the computation of bag of words features, a key challenge is the generation of the codebook. Unsupervised learning is often misguided easily by the data and detects uninteresting patterns within the data. In contrast, we propose to integrate existing knowledge of roof structure and cluster the points of target roof styles into several semantic classes which can then be used as code words in the bag of words model. We use synthetic variants of these code words to train a semantics point classifier. We evaluate our approach on two datasets with different levels of degradations. We compare the results of our approach with two unsupervised learning algorithms: K-Means and Gaussian Mixture Model. We show that our approach achieve higher accuracy in classification of the roof styles and maintains consistent performance among different datasets.

References

1. F. Bernardini, J. Mittleman, H. Rushmeier, C. Silva, and G. Taubin. The ball-pivoting algorithm for surface reconstruction. Visualization and Computer Graphics, IEEE Transactions on, 5(4):349--359, 1999.
2. G. Borgefors. Distance transformations in digital images. Computer vision, graphics, and image processing, 34(3):344--371, 1986.
3. L. Breiman. Random forests. Machine learning, 45(1):5--32, 2001.
4. N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer. Smote: Synthetic minority over-sampling technique. J. Artif. Int. Res., 16(1):321--357, June 2002.
5. A. F. Elaksher and J. s. Bethel. Automatic generation of high-quality three-dimensional urban buildings from aerial images. URISA Journal, 20(1):5--13, 2008.
6. S. O. Elberink and G. Vosselman. Building reconstruction by target based graph matching on incomplete laser data: Analysis and limitations. Sensors, 9: 6101--6118, 2009.
7. S. O. Elberink and G. Vosselman. Target graph matching for building reconstruction. Laserscanning 2009; International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences:, 38, 2009.
8. G. Guennebaud and M. Gross. Algebraic point set surfaces. 26(3):23, 2007.
9. H. Huang and C. Brenner. Rule-based roof plane detection and segmentation from laser point clouds. In Urban Remote Sensing Event (JURSE), 2011 Joint, pages 293--296. IEEE, 2011.
10. A. E. Johnson and M. Hebert. Using spin images for efficient object recognition in cluttered 3d scenes. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 21(5):433--449, 1999.
11. F. Lafarge and C. Mallet. Creating large-scale city models from 3d-point clouds: a robust approach with hybrid representation. International journal of computer vision, 99(1):69--85, 2012.
12. R. Osada, T. Funkhouser, B. Chazelle, and D. Dobkin. Shape distributions. Graphics, ACM Transactions on, 21(4):807--832, 2002.
13. C. Poullis and S. You. Automatic reconstruction of cities from remote sensor data. In Computer Vision and Pattern Recognition (CVPR), 2009 IEEE Computer Society Conference on, pages 2775--2782. IEEE, 2009.
14. R. B. Rusu, Z. C. Marton, N. Blodow, and M. Beetz. Persistent point feature histograms for 3d point clouds. IAS-10, page 119, 2008.
15. S. Valero, J. Chanussot, and P. Gueguen. Classification of basic roof types based on vhr optical data and digital elevation model. In Geoscience and Remote Sensing Symposium (IGARSS), 2008 IEEE International, volume 4, pages IV--149. IEEE, 2008.
16. V. Verma, R. Kumar, and S. Hsu. 3d building detection and modeling from aerial lidar data. In Computer Vision and Pattern Recognition (CVPR), 2006 IEEE Computer Society Conference on, volume 2, pages 2213--2220. IEEE, 2006.
17. G. Vosselman and S. Dijkman. 3d building model reconstruction from point clouds and ground plans. International Archives of Photogrammetry Remote Sensing and Spatial Information Sciences, 34(3/W4):37--44, 2001.
18. Q.-Y. Zhou and U. Neumann. 2.5 d dual contouring: a robust approach to creating building models from aerial lidar point clouds. In Computer Vision (ECCV), 2010 European Conference on, pages 115--128. Springer, 2010.
19. Q.-Y. Zhou and U. Neumann. 2.5d building modeling by discovering global regularities. In Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on, pages 326--333. IEEE, 2012.

Index Terms

• Computing methodologies

  • Artificial intelligence

    • Computer vision

      • Computer vision problems

      • Computer vision tasks

        • Scene understanding