Linear feature extraction from point cloud using color information

Abstract

LiDAR is considered as an effective technology for digitizing the real scene at a very high-resolution and in a short time. However, the resolution of the LiDAR is not sufficient to identify and evaluate the façade surface features like edges and cracks. Generally, photographs provide a better interpretation of the linear characteristics. The complementary benefits of each allow exploring valuable spatial information with different surface detail levels. The paper introduces a flexible image-based approach for linear feature extraction from LiDAR point cloud. Initially, the algorithm converts the point clouds into a structured depth image to reduce the complexity and computation time. Using transformation matrix and camera calibration parameters, the visible point clouds are perceptively projected into color image space using co-linearity equations. The result depth channel is sampled with the interpolation process and added to the color channels to compute (RGBD) layers. The edges and linear features of the surface are initially extracted using the optical 2D imagery and subsequently, each pixel of the linear features can be projected directly into 3D space. Due to the various acquisition positions of the laser and color images, the issue of occlusion is resolved using the visibility algorithm. Applying the methodology, experimental results from the Treasury Monument of Jordan's ancient Petra City, indicate that the developed approach provides adequate contour points for better interpretation and quantification of weathering processes and dangerous cracking. 3D Modeling these features can also reduce data size, facilitating surface inspection and analysis with simpler models.

Color and Laser Data as a Complementary Approach for Heritage Documentation

“…It is difficult to record the entire site solely with a terrestrial laser scan, as the occlusions may render the final point cloud incomplete [89]. TLS enables high-detailed environment recording reach several million 3D points, but data still does not have adequate resolution to create the surface features of the scanned object, such as cracks, finding features in point clouds was not easy [90]. In addition, this abundance of data can also be a drawback, because it creates huge files of point clouds.…”

Documentation of Structural Damage and Material Decay Phenomena in H-Bim Systems

“…The diffusion of HBIM systems, hence the application of object oriented processes regarding the historical built heritage, have highlighted the need to individuate specific procedures for the modeling of elements and characteristics that traditional BIM systems do not allow, looking forward to replicate in the digital space not only the metric dimensions of the buildings, but also what concerns conservation state, hence structural instability and the characterization of the surfaces affected by decay pathologies, in order to organize opportune interventions on them (Alshawabkeh, 2020, Tomalini et al, 2020, Tsilimantou et al, 2020, Brumana et al, 2017, Lo Turco et al, 2017, Bruno et al, 2017, Vidovszky, 2016, Clini et al, 2014, Meschini et al, 2013.…”

“…The most advanced approaches in the research of a fast solution for the representation of decay areas use the chromatic information a 2D picture for data segmentation (Meschini et al, 2013, Tomalini et al, 2020, Lo Turco et al, 2020. Moreover, simple imaging techniques allow the individuation and the extraction (vectorization) of area borders from basic RGB images, as long as they are defined and recognizable (Alshawabkeh, 2020). Despite being vectorized and measurable, plan views do not allow to relate a documented decay pathology and its continuations on orthogonal surfaces, to what is present in the view, leading to a simplification in the evaluation of quantities and to a gap in the data.…”

Investigating Surface Fractures and Materials Behavior of Cultural Heritage Buildings Based on the Attribute Information of Point Clouds Stored in the TLS Dataset

“…Kobayashi et al [17] further employed this color information by calculating Hue (H) and Saturation (S) values in order to identify road signs in Japan. Zhan et al [18] performed color-based segmentation based on colorimetric similarity and spatial proximity to segment ancient Chinese architecture, while Alshawabkeh [19] used it to automatically detect and quantify façade linear features.…”