Point Cloud Filtering Research Articles

Wedge-Filtering of Geomorphologic Terrestrial Laser Scan Data

Terrestrial laser scanning is of increasing importance for surveying and hazard assessments. Digital terrain models are generated using the resultant data to analyze surface processes. In order to determine the terrain surface as precisely as possible, it is often necessary to filter out points that do not represent the terrain surface. Examples are vegetation, vehicles, and animals. Filtering in mountainous terrain is more difficult than in other topography types. Here, existing automatic filtering solutions are not acceptable, because they are usually designed for airborne scan data. The present article describes a method specifically suitable for filtering terrestrial laser scanning data. This method is based on the direct line of sight between the scanner and the measured point and the assumption that no other surface point can be located in the area above this connection line. This assumption is only true for terrestrial laser data, but not for airborne data. We present a comparison of the wedge filtering to a modified inverse distance filtering method (IDWMO) filtered point cloud data. Both methods use manually filtered surfaces as reference. The comparison shows that the mean error and root–mean-square-error (RSME) between the results and the manually filtered surface of the two methods are similar. A significantly higher number of points of the terrain surface could be preserved, however, using the wedge-filtering approach. Therefore, we suggest that wedge-filtering should be integrated as a further parameter into already existing filtering processes, but is not suited as a standalone solution so far.

Digital Elevation Model from the Best Results of Different Filtering of a LiDAR Point Cloud

AbstractThe LiDAR point clouds captured with airborne laser scanning provide considerably more information about the terrain surface than most data sources in the past. This rich information is not simply accessed and convertible to a high quality digital elevation model (DEM) surface. The aim of the study is to generate a homogeneous and high quality DEM with the relevant resolution, as a 2.5D surface. The study is focused on extraction of terrain (bare earth) points from a point cloud, using a number of different filtering techniques accessible by selected freeware. The proposed methodology consists of: (1) assessing advantages/disadvantages of different filters across the study area, (2) regionalization of the area according to the most suitable filtering results, (3) data fusion considering differently filtered point clouds and regions, and (4) interpolation with a standard algorithm. The resulting DEM is interpolated from a point cloud fused from partial point clouds which were filtered with multiscale curvature classification (MCC), hierarchical robust interpolation (HRI), and the LAStools filtering. An important advantage of the proposed methodology is that the selected landscape and datasets properties have been more holistically studied, with applied expert knowledge and automated techniques. The resulting highly applicable DEM fulfils geometrical (numerical), geomorphological (shape), and semantic quality properties.

Application of Constructing Three-Dimensional Model Using Laser Scanning Technology

Starting from the principle of three-dimensional laser scanner and after analyzing the positioning error of point cloud, in this paper the following key problems confronted in data processing are researched ,the problems are range image segmentation, point cloud registration, point cloud filtering, absolute positioning and stitching data, modeling in AutoCAD or 3Dmax. Then, examples were introduced in constructing three-dimensional model using laser scanning technology. The results showed that the use of this technology can provide high-precision building models, which is important in constructing three-dimensional models and very helpful of digital cities.

Terrestrial laser scan error in the presence of dense ground vegetation

AbstractTerrestrial laser scanning (TLS) data‐sets are seeing increasing use in geology, geomorphology, forestry and urban mapping. The ease of use, affordability and operational flexibility of TLS suggest that demand for it is likely to increase in large‐scale mapping studies. However, its advantages may remain restricted to specific environments, because of difficulties in defining bare‐ground level in the presence of ground‐level vegetation. This paper seeks to clarify the component contributions to TLS elevation error deriving from vegetation occlusion, scan co‐registration error, point cloud georeferencing error and target position definition in TLS point cloud data. A multi‐scan single‐returns TLS point cloud data‐set of very high resolution (∼250 points/m2) was acquired for an 11 hectare area of open, substantially flat and 100% vegetated coastal saltmarsh, providing data for the empirical quantification of TLS error. Errors deriving from the sources discussed are quantified, clarifying the potential proportional contribution of vegetation to other error sources. Initial data validation is applied to the TLS point cloud data after application of a local‐lowest‐point selection process, and repeat validation tests are applied to the resulting filtered point cloud after application of a kriging‐based error adjustment and data fusion with GPS. The final results highlight the problem of representing bare ground effectively within TLS data captured in the presence of dense ground vegetation and clarify the component contributions of elevation error deriving from surveying and data processing.

Accuracy improvement of laser line scanning for feature measurements on CMM

Because of its high speed and high detail output, laser line scanning is increasingly included in coordinate metrology applications where its performance can satisfy specified tolerances. Increasing its accuracy will open the possibility to use it in other areas where contact methods are still dominant. Multi-sensor systems allow to select discrete probing or scanning methods to measure part elements. Decision is often based on the principle that tight toleranced elements should be measured by contact methods, while other more loose toleranced elements can be laser scanned. This paper aims to introduce a method for improving the output of a CMM mounted laser line scanner for metrology applications. This improvement is achieved by filtering of the scanner's random error and by combination with widely spread and reliable but slow touch trigger probing. The filtered point cloud is used to estimate the form deviation of the inspected element while few tactile obtained points were used to effectively compensate for errors in the point cloud position.

Intelligent optimization of seam-line finding for orthophoto mosaicking with LiDAR point clouds

A detailed study was carried out to find optimal seam-lines for mosaicking of images acquired by an airborne light detection and ranging (LiDAR) system. High ground objects labeled as obstacles can be identified by delineating black holes from filtered point clouds obtained by filtering the raw laser scanning dataset. An innovative A* algorithm is proposed that can automatically make the seam-lines keep away from these obstacles in the registered images. This method can intelligently optimize the selection of seam-lines and improve the quality of orthophotos. A simulated grid image was first used to analyze the effect of different heuristic functions on path planning. Three subsets of LiDAR data from Xiüan, Dunhuang, and Changyang in Northwest China were obtained. A quantitative method including pixel intensity, hue, and texture was used. With our proposed method, 9.4%, 8.7%, and 9.8% improvements were achieved in Dunhuang, Xiüan, and Changyang, respectively.

Reducing and Filtering Point Clouds With Enhanced Vector Quantization

Modern scanners are able to deliver huge quantities of three-dimensional (3-D) data points sampled on an object's surface, in a short time. These data have to be filtered and their cardinality reduced to come up with a mesh manageable at interactive rates. We introduce here a novel procedure to accomplish these two tasks, which is based on an optimized version of soft vector quantization (VQ). The resulting technique has been termed enhanced vector quantization (EVQ) since it introduces several improvements with respect to the classical soft VQ approaches. These are based on computationally expensive iterative optimization; local computation is introduced here, by means of an adequate partitioning of the data space called hyperbox (HB), to reduce the computational time so as to be linear in the number of data points N, saving more than 80% of time in real applications. Moreover, the algorithm can be fully parallelized, thus leading to an implementation that is sublinear in N. The voxel side and the other parameters are automatically determined from data distribution on the basis of the Zador's criterion. This makes the algorithm completely automatic. Because the only parameter to be specified is the compression rate, the procedure is suitable even for nontrained users. Results obtained in reconstructing faces of both humans and puppets as well as artifacts from point clouds publicly available on the web are reported and discussed, in comparison with other methods available in the literature. EVQ has been conceived as a general procedure, suited for VQ applications with large data sets whose data space has relatively low dimensionality.

Repetitive interpolation: A robust algorithm for DTM generation from Aerial Laser Scanner Data in forested terrain

We present a new algorithm for digital terrain model (DTM) generation from an airborne laser scanning point cloud, called repetitive interpolation (REIN). It is especially applicable in steep, forested areas where other filtering algorithms typically have problems distinguishing between ground returns and off-ground points reflected in the vegetation. REIN can produce a DTM either in a vector grid or in a TIN data structure. REIN is applied after an initial filtering, which involves removal of all negative outliers and removal of many, but not necessarily all, off-ground points by some existing filtering algorithm. REIN makes use of the redundancy in the initially filtered point cloud (FPC) in order to mitigate the effect of the residual off-ground points. Multiple independent random samples are taken from the initial FPC. From each sample, ground elevation estimates are interpolated at individual DTM locations. Because the lower bounds of the distributions of the elevation estimates at each DTM location are almost insensitive to positive outliers, the true ground elevations can be approximated by adding the global mean offset to the lower bounds, which is estimated from the data. The random sampling makes REIN unique among the methods of filtering airborne laser data. While other filters behave deterministically, always generating a filter error in special situations, in REIN, because of its random aspects, these errors do not occur in each sample, and typically cancel out in the final computation of DTM elevations. Reduction of processing time by parallelization of REIN is possible. REIN was tested in a test area of 2 hectares, encompassing steep relief covered by mixed forest. An Optech ALTM 1020 lidar was used, with a flying height of 260–300 m above the ground, the beam divergence was 0.3 mrad, and the obtained point cloud density for the last returns was 8.5 m − 2 . A DTM grid was generated with 1 m horizontal resolution. The root mean square elevation error of the DTM ranged between ± 0.16 m and ± 0.37 m, depending on REIN sampling rate and number of samples taken, the lowest value achieved with 4 samples and using a 23% sampling rate. The paper also gives a short overview on existing filtering algorithms.

Analysis of three-dimensional sinter shrinkage of copings made from alumina in an innovative direct shaping process

The three-dimensional (3D) evaluation and comparison of free-form-surfaces is a complex problem [Dent. Mater. 8 (1992) 49; J. Dent. Res. 76 (1997) 1799; Dent. Mater. 16 (2000) 145; J. Prosthet. Dent. 70 (1993) 457; Dent. Mater. 19 (2003) 19]. However, it is essential in order to analyze the sinter shrinkage of dental ceramic-restorations where isotropic and linear shrinkage is desired for accurate fit on the prepared teeth. In this article, we examine the 3D sinter shrinkage in general and for nine copings from alumina in particular. Using various scaled CAD-models in an iteration scheme, each model was compared to the filtered point cloud of the coping, determining the surface-cloud difference. The magnitude of deviations from linear sinter shrinkage was investigated. Furthermore, a new fabrication process for ceramic-restorations is introduced.

A Binary Morphology-Based Filtering Algorithm for Reverse Engineering

A laser scanner is a common surface measurement tool in reverse engineering (RE). The point cloud obtained from a laser scanner usually has some noisy points. An algorithm based on a binary morphology technique is presented to identify the noisy points. After removing the noisy points, the filtered point cloud can be used to reconstruct the surface of a 3D object. The filtering algorithm described can remove both the points at a far distance from the scanned object and the prods on the surface of the object.