Image-based construction of building energy models using computer vision

Üniversitemiz öğretim üyelerinden Doç. Dr. İpek Gürsel Dino, Doç. Dr. Sinan Kalkan ve Prof. Dr. A. Aydin Alatan’ın yazarları arasında bulunduğu “Image-based construction of building energy models using computer vision” başlıklı makale Automation in Construction’da yayınlandı.

Improving existing buildings' energy performance requires energy models that accurately represent the building. Computer vision methods, particularly image-based 3D reconstruction, can effectively support the creation of 3D building models. In this paper, we present an image-based 3D reconstruction pipeline that supports the semi-automated modeling of existing buildings. We developed two methods for the robust estimation of the building planes from a 3D point cloud that (i) independently estimate each plane and (ii) impose a perpendicularity constraint to plane estimation. We also estimate external walls' thermal transmittance values using an infrared thermography-based method, with the surface temperatures measured by a thermal camera. We validate our approach (i) by testing the pipeline's ability in constructing accurate surface models subject to different image sets with varying sizes and levels of image quality, and (ii) through a comparative analysis between the calculated energy performance metrics of a ground truth and calculated energy simulation model.

Dino, I. G., Sari, A. E., Iseri, O. K., Akin, S., Kalfaoglu, E., Erdogan, B., . . . Alatan, A. A. (2020). Image-based construction of building energy models using computer vision. Automation in Construction, 116 doi:10.1016/j.autcon.2020.103231

Makaleye erişim için: https://www.sciencedirect.com/science/article/abs/pii/S0926580519313287?via%3Dihub

Anahtar sözcükler:

3D modeling; 3D reconstruction; Building energy modeling; Computer vision; Infrared imaging

Diğer Yazarlar:
Sari, A.E. (ODTÜ), Iseri, O.K. (ODTÜ), Akin, S. (ODTÜ), Kalfaoglu, E. (ODTÜ), & Erdogan, B.

Ek Bilgiler:
This work was supported by an Institutional Links grant under the Newton-Katip Celebi partnership, Grant No. 217M519 by the Scientific and Technological Research Council of Turkey (TUBITAK) and ID [ 352335596 ] by British Council , UK. This is a substantially extended and enhanced version of the paper presented at the CIB W78 Annual Conference held at Northumbria University in Newcastle UK in September 2019. We would like to acknowledge the editorial contributions of Professor Bimal Kumar of Northumbria University and Dr. Farzad Rahimian of Teesside University in the publication of this paper. We also would like to thank METU Faculty of Architecture for their support during field tests.