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.

Unmanned aerial vehicles (UAVs) can provide valuable spatial information products for many projects across a wide range of applications. One of the major challenges in this discipline is the quality of positioning accuracy of the resulting mapping products in professional photogrammetric projects. This is especially true when using low-cost UAV systems equipped with GNSS receivers for navigation. In this study, the influence of UAV flight direction and camera orientation on positioning accuracy in an urban area on the west bank of the Euphrates river in Iraq was investigated. Positioning accuracy was tested in this study with different flight directions and camera orientation settings using a UAV autopilot app (Pix4Dcapture software (Ver. 4.11.0)). The different combinations of these two main parameters (camera orientation and flight direction) resulted in 11 different flight cases for which individual planimetric and vertical accuracies were evaluated. Eleven flight sets of dense point clouds, DEMs, and ortho-imagery were created in this way to compare the achieved positional accuracies. One set was created using the direct georeferencing method (without using GCPs), while the other ten sets were created using the indirect georeferencing approach based on ground truth measurements of five artificially created GCPs. Positional accuracy was found to vary depending on the user-defined flight plan settings, despite an approximately constant flight altitude. However, it was found that the horizontal accuracy achieved was better than the vertical accuracy for all flight sets. This study revealed that combining multiple sets of images with different flight directions and camera orientations can significantly improve the overall positional accuracy to reach several centimeters.

We present a hybrid approach for modeling complex interior structural elements from the unstructured point cloud without additional information. The proposed approach focuses on an integrated modeling strategy that can reconstruct structural elements and keep the balance of model completeness and quality. First, a data-driven approach detects the complete structure points of indoor scenarios including the curved wall structures and detailed structures. After applying the down-sampling process to point cloud dataset, ceiling and floor points are detected by RANSAC. The ceiling boundary points are selected as seed points of the growing algorithm to acquire points related to the wall segments. Detailed structures points are detected using the Grid-Slices analysis approach. Second, a model-driven refinement is conducted to the structure points that aims to decrease the impact of point cloud accuracy on the quality of the model. RANSAC algorithm is implemented to detect more accurate layout, and the hole in structure points is repaired in this refinement step. Lastly, the Screened Poisson surface reconstruction approach is conducted to generate the model based on the structure points after refinement. Our approach was validated on the backpack laser dataset, handheld laser dataset, and synthetic dataset, and experimental results demonstrate that our approach can preserve the curved wall structures and detailed structures in the model with high accuracy.

A method designed to reconstruct outdoor 3D building models automatically from a point cloud is presented in this paper. The proposed approach starts with building detection using spectral and spatial data from the UAV point cloud to remove non-building features. RANSAC, modified convex hull, and line growing algorithms are used to extract main roof planes and their boundaries. Roof planes are adjusted to each other using geometrical constraints, the height of each plane is estimated and a 3D model for the whole structure is constructed with LoD2. The key contribution of this approach is using a hybrid approach of model-driven with statistical analysis for modeling complex structures from a noisy point cloud. The reconstructed model shows that the workflow is sufficient to describe the whole building structure in the required LoD.

We present an approach for segmenting an indoor unstructured point cloud into multiple rooms without additional information. Our proposed approach starts by applying a cloth simulation filter (CSF) to the raw dataset to detect point cloud-related ceiling patches without inverting the point cloud. Next, a grid map analysis is conducted for initial room segmentation. It is updated using a morphological erosion process and a neighborhood filter with an adaptive threshold. Finally, boundary recovery is utilized to correct for any incomplete room boundaries obtained from the previous steps. The capabilities and accuracy of our approach were evaluated on different point cloud datasets, and the average recall and precision were 97.13% and 96.60%, respectively. Further validation with the datasets of different levels of noise and registration errors show that this approach achieves an average recall and precision of 99.24% and 99.90%, respectively.