Photogrammetric Point-Cloud Replicability When Documenting Forensic Archaeological Scenes under Variable Lighting Conditions

Recently, terrestrial laser scanners have been significantly improved in terms of accuracy, measurement distance, measurement speed, and resolution. They enable us to capture dense 3D point clouds of large-scale objects and fields, such as factories, engineering plants, large equipment, and transport ships. In addition, the mobile mapping system, which is a vehicle equipped with laser scanners and GPSs, can be used for capturing large-scale point clouds from a wide range of roads, buildings, and roadside objects. Large-scale point clouds are useful in a variety of applications, such as renovation and maintenance of facilities, engineering simulation, asset management, and 3D mapping. To realize these applications, new techniques must be developed for processing large-scale point clouds. So far, point processing has been studied mainly for relatively small objects in the field of computer-aided design and computer graphics. However, in recent years, the application areas of point clouds are not limited to conventional domains, but also include manufacturing, civil engineering, construction, transportation, forestry, and so on. This is because the state-of-the-art laser scanner can be used to represent large objects or fields as dense point clouds. We believe that discussing new techniques and applications related to large-scale point clouds beyond the boundaries of traditional academic fields is very important.This special issue addresses the latest research advances in large-scale point cloud processing. This covers a wide area of point processing, including shape reconstruction, geometry processing, object recognition, registration, visualization, and applications. The papers will help readers explore and share their knowledge and experience in technologies and development techniques.All papers were refereed through careful peer reviews. We would like to express our sincere appreciation to the authors for their submissions and to the reviewers for their invaluable efforts for ensuring the success of this special issue.

In reverse engineering, the point cloud data processing is the key, it will directly affect the quality and efficiency of the subsequent reconstruction of the surface. However, the amount of data obtained by three-dimensional laser scanning is often larger and redundant, which will increase the difficulty of the surface reconstruction, therefore, the point cloud data is needed to streamline. Depth analysis of the efficiency and accuracy of curvature grid method, a secondary streamlined algorithm is proposed, which take use of non-uniform grid streamline law based on octree to streamline to the point cloud, and re-use curvature streamline method based on B-spline surface fitting to streamline it. This method, whose streamline ratio is relatively high, can effectively retain the feature point cloud data, providing a great convenience for the follow-up of reverse engineering.

Fast automatic three-dimensional road model reconstruction based on mobile laser scanning system

The article explores the creation of digital terrain models (DRMs) based on dense point cloud classification in QGIS, Digitals, and 3D Survey software. In the research process, a dense cloud of points created during 3D scanning of the territory of the botanical garden of NUBiP of Ukraine was used, followed by classification and construction of digital relief models in various software tools. The stages of point cloud classification, creation of classification layers, data export to QGIS, Digitals and 3D Survey software tools for further modeling and drawing of models with isolines were studied. The digital model of the relief and the stages of its creation, "cleaning" of the original cloud of points of different density are considered. On the basis of the same initial data, a dense cloud of points, the formation of digital models of the relief of the territory of the botanical garden of NUBiP of Ukraine was carried out in various software tools. The process of creating a digital relief model was investigated based on a dense point cloud using QGIS, Digitals, 3D Survey programs. The main advantages and disadvantages of building a digital relief model based on a cloud of points formed by the results of 3D scanning are analyzed. Features and capabilities of software tools QGIS, Digitals, 3D Survey and their ability to build 3D surfaces based on exported point clouds are considered.

Being able to assess the amount of uncertainty locally associated to dense point clouds generated by measurement can help investigate the relations between the metrological performance of a chosen measuring technology, and the local geometric and surface properties of the measurand geometry. In previous research it was demonstrated that spatial statistics based on Gaussian Random Fields and measurement repeats could be used to obtain spatial maps capturing both local dispersion and local bias associated to the position of points within measured clouds. However, the previous method had scalability limitations when handling very dense point clouds, due to it requiring the resolution of a global, increasingly larger, covariance matrix in order to solve the random field fitting problem. This work presents a variant to the previous method, where the covariance matrix is solved only locally, making the method better scalable to handle denser point clouds. Despite the new method not being able to return an equally rich information content in relation to spatial covariance, it still allows to obtain almost equally accurate information on local bias and variance, with significant gains in terms of processing speed and, importantly, making it now possible to handle very dense clouds which would be unviable to process with the original method.

The 3D modeling of urban buildings has become a common research area in various disciplines such as photogrammetry and computer vision, with different applications such as intelligent city management, navigation of self-driving cars and architecture, just to name a few. The objective of this study is to produce a 3D model of the external facade of the buildings with the required precision, accuracy and level of detail according to the user’s requirements, while minimizing time and cost. This research focuses on the production of 3D models for blocks of residential buildings in Tehran, Iran. The Insta 360 One X2 spherical camera is selected to capture the data due to its low cost and 360 × 180° field of view. The camera specifications have facilitated more efficient data collection in terms of both time and cost. The proposed modeling method is based on extracting lines of external walls through the utilization of the point cloud density concept. Initially, photogrammetric point clouds are generated in with a reconstruction precision of 0.24 m from spherical camera images. In the next step, the 3D point cloud is projected into a 2D point cloud by setting the height component to zero. The 2D point cloud is then rotated based on the direction angle determined by the Hough transform so that the perpendicular walls are parallel to the axes of the coordinate system. Next, a 2D point cloud density analysis is performed by voxelizing the point cloud and counting the number of points in each voxel in both the horizontal and vertical directions. By determining the peaks in the density plot, the lines of the external vertical and horizontal walls are extracted. To extract the diagonal external walls, the density analysis is performed in the direction of the first principal component. Finally, by determining the height of each wall in the point cloud, a 3D model is created at the level of detail one. The resulting model has a precision of 0.32 m compared to real sizes, and the 2D plan has a precision of 0.31 m compared to the ground truth map. The use of the spherical camera and point cloud density analysis makes this method efficient and cost-effective, making it a promising approach for future urban modeling projects.

The aim of this paper is to implement a novel Scan to BIM method applied to cultural heritage field to build 3D GIS model able to connect different databases and, as consequence, allowing a multidisciplinary approach. The proposed method was applied to the case study of a rock church located in the famous ceramics district of Grottaglie (Italy). To obtain the 3D GIS model of the structure under investigation, it was necessary to perform, as a first step, an integrated survey with Terrestrial Laser Scanner technology and close range photogrammetry which allowed the construction of a dense point cloud and high-resolution orthophotos useful for the study of architectural and historical analysis of the elements present within the rock site. Subsequently, the single objects identified using the stratigraphic method, was modelled from point cloud by the development of an original procedure based on the use of some tools developed in Rhinoceros and MeshLAB software that allowed reconstructing the objects in a geometrically accurate way. Indeed, the difference between the point cloud and the BIM model was of few millimetres, demonstrating the high quality of the proposed method. Finally, the BIM model was imported into 3D GIS environment and for each element previously identified, it was possible to connect multi-information.

Point cloud up-sampling network with multi-level spatial local feature aggregation

The generation of dense and accurate point clouds from airborne light detection and ranging (LiDAR) waveform data is crucial to forest inventory. This work proposes a deconvolution method with: 1) an automatic stopping criterion to differentiate near-adjacent targets and 2) an iterative false subwaveform removal algorithm to remove outliers caused by noise. Synthetic waveforms with different overlap rates were processed using the proposed method, the Gaussian decomposition (GD) method, and the Richardson Lucy (RL) deconvolution method. Results showed that: 1) the number of subwaveforms detected by the proposed method is 9% higher than that of the RL and 20% higher than that of the GD when the overlap rates are larger than 0.6 and 2) the proposed method is of the smallest ground and peak distance errors. Results of the indoor experiment also show that the proposed method is superior in finding near targets meanwhile leading to small ground and peak distance error. Furthermore, the proposed method was tested by airborne waveforms from the Dagujia forest farm. The point cloud density acquired by the proposed method is 3% and 35% higher than that by the RL and GD method. Fewer outliers are produced by the proposed method. The number of individual trees extracted from the proposed point clouds is 22%, 51%, and 57% greater than those extracted from the RL, the GD, and the reference point clouds using the canopy height model-based method. Best individual tree extraction result is produced by the proposed method, especially for an area with small trees.

Using Fast Point Feature Histograms feature from point cloud for 3D object recognition or registration, Fast Point Feature Histograms feature descriptor is arbitrarily and inefficiently calculated by subjectively adjusting the neighborhood radius, and the whole process can’t be completed automatically. An adaptive neighborhood-selection Fast Point Feature Histograms point cloud feature extraction algorithm has proposed to solve this problem. Firstly, we estimate the point cloud density of many pairs of point clouds. Secondly, compute the neighborhood radius to extract the Fast Point Feature Histograms features for Sample Consensus Initial Alignment registration, and count the radius and the density when the registration performance is optimal, and then the cubic spline interpolation fitting is used to obtain the function expression of the radius and the density. Finally, the Fast Point Feature Histograms feature extraction algorithm has combined with the function to form adaptive neighborhood-selection Fast Point Feature Histograms feature extraction algorithm. The experimental results have shown that the proposed algorithm can adaptively choose the appropriate neighborhood radius according to the density of point cloud, and improve the Fast Point Feature Histograms feature matching performance. At the same time, it is improved the computing speed to a better value, which is of guiding significance.

Suppose a large and dense point cloud of an object with complex geometry is available that can be approximated by a smooth univariate function. In general, for such point clouds the “best” approximation using the method of least squares is usually hard or sometimes even impossible to compute. In most cases, however, a “near-best” approximation is just as good as the “best”, but usually much easier and faster to calculate. Therefore, a fast approach for the approximation of point clouds using Chebyshev polynomials is described, which is based on an interpolation in the Chebyshev points of the second kind. This allows to calculate the unknown coefficients of the polynomial by means of the Fast Fourier transform (FFT), which can be extremely efficient, especially for high-order polynomials. Thus, the focus of the presented approach is not on sparse point clouds or point clouds which can be approximated by functions with few parameters, but rather on large dense point clouds for whose approximation perhaps even millions of unknown coefficients have to be determined.

Tracking of multiple extended three-dimensional targets in the presence of partial occlusions from point cloud measurement sets remains a challenge but has obvious applications in autonomous vehicles. We demonstrate that existing range-image based approaches to point cloud segmentation and motion detection can be fused with fast point cloud registration techniques to aid track association. Consequently, multiple generic extended targets in point clouds can be tracked. We also show that robust occlusion handling can be achieved using temporally accumulated geometric information from co-registered point clouds in a static local reference frame. The occlusion logic is based on a novel probabilistic occlusion reasoning approach combined with a simulated geometric handling of self-occlusions. The proposed tracking pipeline was tested on synthetic target geometries superimposed on real-world backgrounds, as well as against measured data from benchmark autonomous driving datasets. The targets tracked encountered both artificially introduced and naturally occurring foreground occlusions as well as self-occlusions. Results show notable robustness to foreground occlusions. Incorrect track associations are possible when multiple targets in proximity display similar dynamics and possess nearly identical geometries. Our sequence of tracking algorithms relies on multiple tunable hyper-parameters. These parameters require further automated runtime-optimization before robust, real-time field application can be achieved.

When filtering highly rugged terrain from dense point clouds (particularly in technical applications such as civil engineering), the most widely used filtering approaches yield suboptimal results. Here, we proposed and tested a novel ground-filtering algorithm, a multi-size voxel cube (MSVC), utilizing a deep neural network. This is based on the voxelization of the point cloud, the classification of individual voxels as ground or non-ground using surrounding voxels (a “voxel cube” of 9 × 9 × 9 voxels), and the gradual reduction in voxel size, allowing the acquisition of custom-level detail and highly rugged terrain from dense point clouds. The MSVC performance on two dense point clouds, capturing highly rugged areas with dense vegetation cover, was compared with that of the widely used cloth simulation filter (CSF) using manually classified terrain as the reference. MSVC consistently outperformed the CSF filter in terms of the correctly identified ground points, correctly identified non-ground points, balanced accuracy, and the F-score. Another advantage of this filter lay in its easy adaptability to any type of terrain, enabled by the utilization of machine learning. The only disadvantage lay in the necessity to manually prepare training data. On the other hand, we aim to account for this in the future by producing neural networks trained for individual landscape types, thus eliminating this phase of the work.

Mobile vision-based autonomous vehicles use video frames from multiple angles to construct a 3D model of their environment. In this paper, we present a post-processing adaptive noise suppression technique to enhance the quality of the computed 3D model. Our near real-time reconstruction algorithm uses each pair of frames to compute the disparities of tracked feature points to translate the distance a feature has traveled within the frame in pixels into real world depth values. As a result these tracked feature points are plotted to form a dense and colorful point cloud. Due to the inevitable small vibrations in the camera and the mismatches within the feature tracking algorithm, the point cloud model contains a significant amount of misplaced points appearing as noise. The proposed noise suppression technique utilizes the spatial information of each point to unify points of similar texture and color into objects while simultaneously removing noise dissociated with any nearby objects. The noise filter combines all the points of similar depth into 2D layers throughout the point cloud model. By applying erosion and dilation techniques we are able to eliminate the unwanted floating points while retaining points of larger objects. To reverse the compression process, we transform the 2D layer back into the 3D model allowing points to return to their original position without the attached noise components. We evaluate the resulting noiseless point cloud by utilizing an unmanned ground vehicle to perform obstacle avoidance tasks. The contribution of the noise suppression technique is measured by evaluating the accuracy of the 3D reconstruction.

Sensor miniaturisation, improved battery technology and the availability of low-cost yet advanced Unmanned Aerial Vehicles (UAV) have provided new opportunities for environmental remote sensing. The UAV provides a platform for close-range aerial photography. Detailed imagery captured from micro-UAV can produce dense point clouds using multi-view stereopsis (MVS) techniques combining photogrammetry and computer vision. This study applies MVS techniques to imagery acquired from a multi-rotor micro-UAV of a natural coastal site in southeastern Tasmania, Australia. A very dense point cloud ( < 1–3 cm point spacing) is produced in an arbitrary coordinate system using full resolution imagery, whereas other studies usually downsample the original imagery. The point cloud is sparse in areas of complex vegetation and where surfaces have a homogeneous texture. Ground control points collected with Differential Global Positioning System (DGPS) are identified and used for georeferencing via a Helmert transformation. This study compared georeferenced point clouds to a Total Station survey in order to assess and quantify their geometric accuracy. The results indicate that a georeferenced point cloud accurate to 25–40 mm can be obtained from imagery acquired from ~50 m. UAV-based image capture provides the spatial and temporal resolution required to map and monitor natural landscapes. This paper assesses the accuracy of the generated point clouds based on field survey points. Based on our key findings we conclude that sub-decimetre terrain change (in this case coastal erosion) can be monitored.