Adaptive noise suppression technique for dense 3D point cloud reconstructions from monocular vision

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

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.

This paper presents a standardized quality criteria to evaluate the 3D point cloud model of the indoor building which is based on point cloud's data accuracy, the prior characteristics of the building and the coincidence errors of the point cloud model. Our assessment framework involves three steps: the point cloud data acquisition, model generation and quality evaluation. In model generation progress, incapacity of scanning the whole building information one time since its multi-storied spatial structure, the building model need to be registered and merged. In evaluation step, taking into account the need of mapping, indoor location and navigation, the establishment of an interior spatial building model requires accurate measurement. Therefore, we adopt data noise analysis to give a judgement. Then, since the geometric characteristics of the building model are varying, the geometric analysis is proposed to evaluate acquisition errors and registration error. Comparative experiments demonstrate our method give integrate, realistic and reliable quality framework for the indoor building point cloud model.

Abstract. Photorealistic 3D models play an important role in various applications related to smart cities. Texture mapping plays a crucial role as the last step of 3D reconstruction. The texture of the model directly reflects the visualization and realism of the model. In complex city scenes, objects often have varying degrees of occlusion from one another. Traditional methods to circumvent occlusion by image or model data are not very effective. In this paper, we propose an automatic texture mapping method for 3D models to circumvent occlusion through 3D spatial through-view relationships between images, models, and point clouds. First of all, calculate the density and voxel resolution of the point clouds, the point clouds here can be either dense matching point clouds or laser point clouds. Voxelization is based on point clouds, voxels generated by voxelization can reflect the spatial location of the point clouds. Secondly, to determine whether there is an occlusion of voxels between the image and the model vertices based on the spatial through-view relationship between the image, the model, and the voxels, and filter out the unobstructed images. Finally, by optimizing the data items of the Markov random field, the shading function is added to the original data items for evaluating the shading condition, based on which the most suitable image is selected for texture mapping of a triangular surface of the model. Loop computation to complete the unobstructed texture mapping of the entire model. In this paper, the public test datasets released by the International Society for Photogrammetry and Remote Sensing (ISPRS) and several urban area datasets collected by ourselves are incorporated to test the performance of the proposed method. Both qualitative and quantitative cross-sectional comparisons with existing texture mapping algorithms are conducted and presented. Experimental results indicate that our method can effectively circumvent occlusion for automated texture mapping, and generate 3D models with fewer obstructed textures, which largely improves the visual quality and realism of the models.

Abstract. Photo-realistic 3D model of object cannot be created by using conventional Total Station method. The aim of this study is to generate 3D building model using UAV’s dense point clouds. In this research, the ability of remotely detected information for the construction of 3D building models is found to be as specific as UAV datasets, Tacheometry Survey and Traverse Survey. Pix4D and MeshLab software were used in this study to perform image processing of 3D building. The building was built up upon using the information needed from capturing UAV images. This procedure requires the height of building to be measured through conventional method such that of UAV and the height is then compared with a 3D building model. At the end of the process, the comparison of height of building, also known as RMSE, was conducted. The images of building were then processed in Photogrammetry which is the Pix4D software. Using Pix4D, this study proceeded with several processing stages such as camera calibration, photo uploading, building of dense cloud, mesh generation, and model texturing. Those processes were done automatically by using Pix4D tools. High quality of photo-realistic 3D model of building was then successfully constructed. The accuracy assessment was done by comparing the measurement of 3D model of building. Direct measuring of building by using total station had taken place to compare the measurements between 3D model details with actual measurements. The measurement of 3D model of building was done in Pix4D software. The RMSE achieved in this study is 0.015 m which is under reasonable accuracy limit. This study proves that UAV point cloud is capable to produce a very fine 3D model.

<p><span xml:lang="PT-BR" data-contrast="auto"><span>The use of digital outcrop models (DOMs) in geosciences has been increasingly common since the beginning of the 2000s due to the technological advances in the positional and image-quality field equipment, data collection, and processing with practicality, agility, and high accuracy even in inaccessible sites. DOMs incorporate all the visual elements that geoscientists analyze in the outcrops as 3D models with a spatial resolution of a few millimeters per pixel and positional accuracy of less than 2 cm. The great advantage of DOMs is their daily availability for visual inspection and interpretation in the office, complementing the data analysis performed in the field. Therefore, the continuous development of high accuracy and dense image-based and point cloud models has been crucial for quantitative approaches using digital models. Another challenge in this process involves the development of tools and methodologies for interpretation of DOMs, especially analysis and interpretation of linear and planar features such as lineations, paleocurrents, joints, faults, and deformation bands. This study aims to systematize the manual and semi-automatic methods of plane extraction using tools (e.g., Compass and Facets) available in the open-source software such as the CloudCompare, and statistically analyze the structural measurements from the extracted data. In this work, we analyzed two 3D integrated ground-UAV photogrammetric models reconstructed with the Structure from Motion (SfM) technique. The study areas are part of the Araripe Basin basement, located in Northeastern Brazil, and represent two case studies involving joints and faults associated with the damage zone of the boundary fault. Initial results obtained by the automatic planes measured with the Facets plugin show distinct fracturing patterns. This is mainly due to the difference of the rock rheology and competence. In the metasedimentary outcrop, we identified 731 planes in phyllites, reduced to </span><span>459 real</span><span> planes after noise remotion and visual inspection during interpretation. In this case, the data accuracy is 62% for plane recognition. The preferential orientation is N40-90E and N40-80W, with high dip angles, and subordinately N45E and N10W with low dip angles. In the metatonalite, 347 planes were recognized, but only 38 of them showed to be real planes, totalizing accuracy of 10,9%. The planes validated </span><span>as real</span><span> indicate a preferential orientation of N10-15W with high angles of dip. Both outcrops used the same processing routine and configuration. The difference observed in the number of planes automatically recognized in each outcrop is a consequence of the relationship between the plane orientation x outcrop orientation, spatial resolution of the model, and the degree of weathering. Besides that, positional accuracy and visual quality are crucial for accurate quantitative interpretation of structural features using digital outcrop models, as well as a well-defined data processing routine and careful inspection of the results by an expert. The data obtained from this methodological approach will contribute to quantitative approaches in structural geology based on robust datasets.</span></span><span> </span></p>

The reverse engineering applications has gained great momentum in industrial production with developments in the fields of computer vision and computer-aided design (CAD). The reproduction of an existing product or a spare part, reproduction of an existing surface, elimination of the defect or improvement of the available product are the goals of industrial reverse engineering applications. The first and the most important step in reverse engineering applications is the generation of the three dimensional (3D) metric model of an existing product in computer environment. After this stage, many operations such as the preparation of molds for mass production, the performance testing, the comparison of the existing product with other products and prototypes which are available on the market are performed by using the generated 3D models. In reverse engineering applications, the laser scanner system or digital terrestrial photogrammetry methods, also called contactless method, are preferred for the generation of the 3D models. In particular, terrestrial photogrammetry has become a popular method since require only photographs for the 3-dimensional drawing, the generation of the dense point cloud using the image matching algorithms and the orthoimage generation as well as its low cost. In this paper, an industrial application of 3D information modelling is presented which concerns the measurement and 3D metric modelling of the ship model. The possible usage of terrestrial photogrammetry in reverse engineering application is investigated based on low cost photogrammetric system. The main aim was the generation of the dense point cloud and 3D line drawing of the ship model by using terrestrial photogrammetry, for the production of the ship in real size as a reverse engineering application. For this purpose, the images were recorded with digital SLR camera and orientations have been performed. Then 3D line drawing operations, point cloud and orthoimage generations have been accomplished by using PhotoModeler software. As a result of the proposed terrestrial photogrammetric steps, 0.5 mm spaced dense point cloud and orthoimage have been generated. The obtained results from experimental study were discussed and possible use of proposed methods was evaluated for reverse engineering application.

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.

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.

GrainPointNet: A deep-learning framework for non-invasive sorghum panicle grain count phenotyping

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.

This paper focuses on the analysis and improvement of K-Neighborhood search of point cloud model, local detail extraction of point cloud model and fusion and transition technology between point cloud models. Through the steps of point cloud scaling, RBF surface reconstruction, segmentation of point cloud to form transition region and uniform resampling of transition region, and the transition point cloud are generated, so the point cloud distribution can be uniform and consistent with the density of the point cloud to be fused. By the method of fusion design, various segmentation and combination of different art models are realized, and it is realized in the file management, view management, data management and other modules of CAD. The final test results show that the effectiveness and robustness of this scheme is of great significance to improve the efficiency and quality of art design.

Abstract. Thanks to the recent worldwide spread of drones and to the development of structure from motion photogrammetric software, UAV photogrammetry is becoming a convenient and reliable way for the 3D documentation of built heritage. Hence, nowadays, UAV photogrammetric surveying is a common and quite standard tool for producing 3D models of relatively large areas. However, when such areas are large, then a significant part of the generated point cloud is often of minor interest. Given the necessity of efficiently dealing with storing, processing and analyzing the produced point cloud, some optimization step should be considered in order to reduce the amount of redundancy, in particular in the parts of the model that are of minor interest. Despite this can be done by means of a manual selection of such parts, an automatic selection is clearly much more viable way to speed up the final model generation. Motivated by the recent development of many semantic classification techniques, the aim of this work is investigating the use of point cloud optimization based on semantic recognition of different components in the photogrammetric 3D model. The Girifalco Fortress (Cortona, Italy) is used as case study for such investigation. The rationale of the proposed methodology is clearly that of preserving high point density in the model in the areas that describe the fortress, whereas point cloud density is dramatically reduced in vegetated and soil areas. Thanks to the implemented automatic procedure, in the considered case study, the size of the point cloud has been reduced by a factor five, approximately. It is worth to notice that such result has been obtained preserving the original point density on the fortress surfaces, hence ensuring the same capabilities of geometric analysis of the original photogrammetric model.

Aiming at the problems of low accuracy and low efficiency of traditional point cloud target classification methods, this paper designs a new classification method based on improved random forest algorithm. Bagging is combined with random subspace to form a subset of feature training at random, so that the generalisation ability of random forest algorithm can be increased while the data processing speed can be accelerated to avoid overfitting phenomenon. On the basis of extracting geometric features of coloured point clouds, the optimal feature subset for classification is determined, and then the dense matching point clouds are classified using the improved random forest algorithm. Experimental results show that the classification error rate of this method is less than 1%, the average classification process takes only 83.995 s, and the VIM value is all over 0.1, indicating that this method can effectively improve the classification effect of dense matching point cloud targets.