Papers
Topics
Authors
Recent
2000 character limit reached

Use of BIM Data as Input and Output for Improved Detection of Lighting Elements in Buildings (2312.11375v1)

Published 18 Dec 2023 in cs.CV and cs.AI

Abstract: This paper introduces a complete method for the automatic detection, identification and localization of lighting elements in buildings, leveraging the available building information modeling (BIM) data of a building and feeding the BIM model with the new collected information, which is key for energy-saving strategies. The detection system is heavily improved from our previous work, with the following two main contributions: (i) a new refinement algorithm to provide a better detection rate and identification performance with comparable computational resources and (ii) a new plane estimation, filtering and projection step to leverage the BIM information earlier for lamps that are both hanging and embedded. The two modifications are thoroughly tested in five different case studies, yielding better results in terms of detection, identification and localization.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (34)
  1. B. Succar, “Building information modelling framework: A research and delivery foundation for industry stakeholders,” Automation in Construction, vol. 18, no. 3, pp. 357 – 375, 2009.
  2. L. Sanhudo, N. Ramos, J. Poças Martins, R. Almeida, E. Barreira, M. Simões, and V. Cardoso, “Building information modeling for energy retrofitting – A review,” Renewable and Sustainable Energy Reviews, vol. 89, pp. 249–260, 2018.
  3. Y. Lu, Z. Wu, R. Chang, and Y. Li, “Building Information Modeling (BIM) for green buildings: A critical review and future directions,” Automation in Construction, vol. 83, pp. 134 – 148, 2017.
  4. M. R. Asl, S. Zarrinmehr, M. Bergin, and W. Yan, “BPOpt: A framework for BIM-based performance optimization,” Energy and Buildings, vol. 108, pp. 401 – 412, 2015.
  5. F. Troncoso-Pastoriza, P. Eguía-Oller, R. P. Díaz-Redondo, and E. Granada-Álvarez, “Generation of BIM data based on the automatic detection, identification and localization of lamps in buildings,” Sustainable Cities and Society, vol. 36, pp. 59 – 70, 2018.
  6. F. Troncoso-Pastoriza, J. López-Gómez, and L. Febrero-Garrido, “Generalized Vision-Based Detection, Identification and Pose Estimation of Lamps for BIM Integration,” Sensors, vol. 18, no. 7, 2018.
  7. L. Pérez-Lombard, J. Ortiz, and C. Pout, “A review on buildings energy consumption information,” Energy and Buildings, vol. 40, no. 3, pp. 394 – 398, 2008.
  8. P. Waide and S. Tanishima, Light’s labour’s lost: policies for energy-efficient lighting: in support of the G8 plan of action. OECD/IEA Paris, 2006.
  9. P. K. Soori and M. Vishwas, “Lighting control strategy for energy efficient office lighting system design,” Energy and Buildings, vol. 66, pp. 329 – 337, 2013.
  10. A. Baloch, P. Shaikh, F. Shaikh, Z. Leghari, N. Mirjat, and M. Uqaili, “Simulation tools application for artificial lighting in buildings,” Renewable and Sustainable Energy Reviews, vol. 82, pp. 3007–3026, 2018.
  11. B. Welle, Z. Rogers, and M. Fischer, “BIM-Centric Daylight Profiler for Simulation (BDP4SIM): A methodology for automated product model decomposition and recomposition for climate-based daylighting simulation,” Building and Environment, vol. 58, pp. 114 – 134, 2012.
  12. L. Díaz-Vilariño, H. González-Jorge, J. Martínez-Sánchez, and H. Lorenzo, “Automatic LiDAR-based lighting inventory in buildings,” Measurement: Journal of the International Measurement Confederation, vol. 73, pp. 544–550, 2015.
  13. C. D. Elvidge, D. M. Keith, B. T. Tuttle, and K. E. Baugh, “Spectral Identification of Lighting Type and Character,” Sensors, vol. 10, no. 4, pp. 3961–3988, 2010.
  14. H. Liu, Q. Zhou, J. Yang, T. Jiang, Z. Liu, and J. Li, “Intelligent Luminance Control of Lighting Systems Based on Imaging Sensor Feedback,” Sensors, vol. 17, no. 2, 2017.
  15. F. Viksten, P.-E. Forssén, B. Johansson, and A. Moe, “Comparison of Local Image Descriptors for Full 6 Degree-of-freedom Pose Estimation,” in Proceedings of the 2009 IEEE International Conference on Robotics and Automation, ICRA’09, (Piscataway, NJ, USA), pp. 1139–1146, IEEE Press, 2009.
  16. F. Tombari, A. Franchi, and L. Di, “BOLD Features to Detect Texture-less Objects,” in 2013 IEEE International Conference on Computer Vision, pp. 1265–1272, Dec 2013.
  17. H. G. Barrow, J. M. Tenenbaum, R. C. Bolles, and H. C. Wolf, “Parametric Correspondence and Chamfer Matching: Two New Techniques for Image Matching,” in Proceedings of the 5th International Joint Conference on Artificial Intelligence - Volume 2, IJCAI’77, (San Francisco, CA, USA), pp. 659–663, Morgan Kaufmann Publishers Inc., 1977.
  18. G. Borgefors, “Hierarchical chamfer matching: a parametric edge matching algorithm,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 10, pp. 849–865, Nov 1988.
  19. J. Shotton, A. Blake, and R. Cipolla, “Multiscale Categorical Object Recognition Using Contour Fragments,” IEEE Transactions on Pattern Analysis and Machine Intelligence., vol. 30, pp. 1270–1281, July 2008.
  20. M. Liu, O. Tuzel, A. Veeraraghavan, and R. Chellappa, “Fast directional chamfer matching,” in 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1696–1703, June 2010.
  21. M. Imperoli and A. Pretto, “D2CO: Fast and robust registration of 3D textureless objects using the Directional Chamfer Distance,” in Proc. of 10th International Conference on Computer Vision Systems (ICVS 2015), pp. 316–328, 2015.
  22. Addison-Wesley Professional, 8th ed., 2013.
  23. R. G. von Gioi, J. Jakubowicz, J. M. Morel, and G. Randall, “LSD: A Fast Line Segment Detector with a False Detection Control,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 4, pp. 722–732, 2010.
  24. M.-Y. Liu, O. Tuzel, A. Veeraraghavan, Y. Taguchi, T. Marks, and R. Chellappa, “Fast object localization and pose estimation in heavy clutter for robotic bin picking.,” International Journal of Robotic Research - IJRR, vol. 31, pp. 951–973, 07 2012.
  25. “gbXML - An industry supported standard for storing and sharing building properties between 3D Architectural and Engineering Analysis Software.” http://www.gbxml.org. Last accessed 8 May 2019.
  26. P. H. S. Torr and A. Zisserman, “MLESAC: A new robust estimator with application to estimating image geometry,” Computer Vision and Image Understanding, vol. 78, pp. 138–156, 2000.
  27. E. Marder-Eppstein, “Project Tango,” in ACM SIGGRAPH 2016 Real-Time Live!, SIGGRAPH ’16, (New York, NY, USA), pp. 40:25–40:25, ACM, 2016.
  28. G. Bradski and A. Kaehler, Learning OpenCV: Computer Vision in C++ with the OpenCV Library. O’Reilly Media, Inc., 2nd ed., 2013.
  29. A. Filgueira, P. Arias, M. Bueno, and S. Lagüela, “Novel inspection system, backpack-based, for 3d modelling of indoor scenes,” in Proceedings of the International Conference on Indoor positioning and Navigation, Alcalá de Henares, Spain, pp. 4–7, 2016.
  30. K. Khoshelham, L. Díaz Vilariño, M. Peter, Z. Kang, and D. Acharya, “The ISPRS benchmark on indoor modelling,” ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XLII-2/W7, pp. 367–372, 2017.
  31. G. Bradski, “OpenCV,” Dr. Dobb’s Journal of Software Tools, vol. 120, pp. 122–125, 2000.
  32. M. Botsch, S. Steinberg, S. Bischoff, and L. Kobbelt, “OpenMesh: A Generic and Efficient Polygon Mesh Data Structure,” in OpenSG Symposium 2002, 2002.
  33. S. Agarwal, K. Mierle, and Others, “Ceres Solver.” http://ceres-solver.org. Last accessed 8 May 2019.
  34. P. Rosin, “Computing global shape measures,” Handbook of Pattern Recognition and Computer Vision, 3rd Edition, pp. 177–196, 2005.
Citations (10)
View on Semantic Scholar

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now
Dice Question Streamline Icon: https://streamlinehq.com

Open Problems

We haven't generated a list of open problems mentioned in this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Generate Now
Lightbulb Streamline Icon: https://streamlinehq.com

Continue Learning

We haven't generated follow-up questions for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Generate Now
List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.

User Circle Single Streamline Icon: https://streamlinehq.com Sign Up