Supervised Detection of Façade Openings in 3D Point Clouds with Thermal Attributes

Abstract

Targeted energy management and control is becoming an increasing concern in the building sector. Automatic analyses of thermal data, which minimize the subjectivity of the assessment and allow for large-scale inspections, are therefore of high interest. In this study, we propose an approach for a supervised extraction of façade openings (windows and doors) from photogrammetric 3D point clouds attributed to RGB and thermal infrared (TIR) information. The novelty of the proposed approach is in the combination of thermal information with other available characteristics of data for a classification performed directly in 3D space. Images acquired in visible and thermal infrared spectra serve as input data for the camera pose estimation and the reconstruction of 3D scene geometry. To investigate the relevance of different information types to the classification performance, a Random Forest algorithm is applied to various sets of computed features. The best feature combination is then used as an input for a Conditional Random Field that enables us to incorporate contextual information and consider the interaction between the points. The evaluation executed on a per-point level shows that the fusion of all available information types together with context consideration allows us to extract objects with 90% completeness and 95% correctness. A respective assessment executed on a per-object level shows 97% completeness and 88% accuracy.

3D Thermal Mapping of Architectural Heritage

“…The low spatial resolution of the thermal images can impose a severe obstacle to the proper reconstruction of the surveyed structures' geometry. Moreover, the radiometric characteristics of TIR images make the direct use of SfM challenging [32]: for this reason, some critical issues are generally encountered during the tie-points extraction phase [34], especially if the acquisition has been performed without following rigorous photogrammetric overlapping criteria.…”

“…This approach requires the registration of both thermal and optical images in the same reference system, which is based on the identification of the Ground Control Points (GCPs) also in thermal images, a challenging task due to the intrinsic characteristics of this kind of data. This problem can be solved using the image geo-tag [34], which is often embedded in the acquired images (if the system used is equipped with a GNSS receiver). However, this solution's positional accuracy is generally low (few meters) unless RTK-enabled platforms are adopted [37].…”

A Hand-Held Sensor System for Exploration and Thermal Mapping of Volcanic Fumarole Fields

“…3D reconstruction with thermal mapping has a wide range of applications, such as in energy-efficiency monitoring of buildings [ 12 , 19 ], Object Detection [ 21 ] or even fruit tree characterization [ 38 ], but also critical applications such as hotspot and fire detection [ 15 , 28 ] in buildings. Related to fumarole field monitoring, Lewis et al .…”

Enhanced 3D Point Cloud from a Light Field Image

“…One of the benefits of using image-based modelling is the accessibility of texture information that can enable material recognition and 3D Computer-Aided Design (CAD) model object recognition [11,12]. Moreover, some works used the reconstruction of 3D scene geometry for the purpose of control and management of energy in the field of 3D modelling of buildings [13]. Pileun et al [14] estimated the positions and orientations of the object by using Simultaneous Localization and Mapping (SLAM).…”