Semantic Geometric Modelling of Unstructured Indoor Point Cloud

Abstract

A method capable of automatically reconstructing 3D building models with semantic information from the unstructured 3D point cloud of indoor scenes is presented in this paper. This method has three main steps: 3D segmentation using a new hybrid algorithm, room layout reconstruction, and wall-surface object reconstruction by using an enriched approach. Unlike existing methods, this method aims to detect, cluster, and model complex structures without having prior scanner or trajectory information. In addition, this method enables the accurate detection of wall-surface “defacements”, such as windows, doors, and virtual openings. In addition to the detection of wall-surface apertures, the detection of closed objects, such as doors, is also possible. Hence, for the first time, the whole 3D modelling process of the indoor scene from a backpack laser scanner (BLS) dataset was achieved and is recorded for the first time. This novel method was validated using both synthetic data and real data acquired by a developed BLS system for indoor scenes. Evaluating our approach on synthetic datasets achieved a precision of around 94% and a recall of around 97%, while for BLS datasets our approach achieved a precision of around 95% and a recall of around 89%. The results reveal this novel method to be robust and accurate for 3D indoor modelling.

Automatic Recognition of Common Structural Elements from Point Clouds for Automated Progress Monitoring and Dimensional Quality Control in Reinforced Concrete Construction

“…Walls are in vertical planes orthogonal to the floors/ceilings [36]; • Walls span from the floor to the ceiling [37]; • Segment sizes of permanent components (walls, floors) are likely larger than clutter [38]; and so on.…”

“…The RANSAC method, proposed in [32], was employed in [36,41,42] with thresholds tuned to the specific dataset to extract planar surfaces of existing indoor rooms before semantic object extraction. In [36], planar segments whose normal vector was parallel and perpendicular to the x−y plane were then considered as floor/ceilings and walls, respectively. In [42], slabs and walls were detected when two proximate planar surfaces with parallel normal vector in opposing directions were found.…”

“…Many previous studies focused on the extraction of architectural and exterior components, such as walls and windows, from existing buildings (after completion of construction) [36][37][38][40][41][42]44]. However, point clouds acquired from construction sites contain outliers due to dust, occlusions, and moving objects, which requires additional robust outlier removal procedures [22].…”

Energy Function Algorithm for Detection of Openings in Indoor Point Clouds

“…Those methods differ on their degree of automatization and their field of application. Some of them were especially designed for indoor segmentation (Wenzhong et al, 2019) while others were only applied to exterior façades (Zolanvari et al, 2018). Recent studies highlight the success of deep learning convolutional neural network (CNN) for point cloud segmentation.…”

“…Some of those openings are neither windows nor doors but the shape of objects that obstructed the wall during the acquisition. We associate each cluster to an energy function with two terms: data and coherence as suggested by Boykov et al (2001) and Wenzhong et al (2019). The data term depends on the shape of the opening.…”