Using an array of overlapping images, such as those collected using a drone, the Pixels to Points tool generates a high-density point cloud based on user-selectable parameters. Employing the principles of photogrammetry in which measurements are derived from photographs, the Pixels to Points tool analyzes the relationship between recognizable objects in adjacent images to determine the three-dimensional coordinates of the corresponding surface. As a by-product of the point cloud generation capability, the Pixels to Points tool also offers the option of creating an orthorectified image by gridding the RGB values in each point, as well as a 3D Mesh, complete with photorealistic textures.

The processing of creating the point cloud begins with a simple loading of the images into the Pixels to Points dialog box. For optimal results, at least 60% overlap and evenly distributed photos taken from varying angles are recommended. Individual images can be previewed and those not needed for the final point cloud can be removed. Various settings can then be applied to determine the output quality, analysis method, etc. Finally, ground control points may optionally be added to adjust the horizontal and vertical positioning of the point cloud. After processing is complete, the point cloud will be automatically added to the current workspace. It can be further processed or edited before exporting to any of the supported point cloud formats including LAS and LAZ.

The Pixels to Points process is memory intensive and may take several hours to process depending on the input data and quality setting. It is recommended that this process is performed on a dedicated computer with at least 16GB of RAM. The Pixels to Points tool also requires a 64-bit operating system.

Using a selected group of lidar points, this process uses the inherent 3D geometry of the points along with the associated colors if present and creates a 3D mesh or model. When viewed in 3D, this model displays as a multifaceted photo-realistic 3D representation of the corresponding feature. This process produces a similar output to the model creation option in the Pixels to Points tool.

Automatic Point Classification Algorithms

The Lidar Module includes a convenient toolbar to streamline the classification of selected points with a single button click. This toolbar includes buttons for the most common classification types including ground, vegetation, and buildings. Additionally, selected points can be manually edited and assigned to any of the ASPRS lidar classification types using the Digitizer’s edit function.

Based on the geometric properties and other characteristics of the lidar file or point cloud, the Lidar Module’s automatic reclassification tool is able to accurately identify and automatically reclassify points representing the important point feature types. First and foremost is the identification of ground points, which is used for the creation of a DTM or bare-earth model. Within the remaining above-ground points, specific algorithms can be applied to identify and reclassify high vegetation, buildings, and powerlines.

The Lidar Module offers a tool for verifying the vertical accuracy of a point cloud. Using surveyed ground control points, the elevation values throughout the layer can be checked and adjusted if necessary.

Points can be filtered using a variety of criteria and at various stages during the point cloud processing workflow. During import, points can be filtered based on classification, return count, sample count, or based on their geographic distribution. The same filtering options can be used to filter the display of points in the map view. When creating a gridded surface or a DEM, a further level of filtering is available which can be used to remove points based on elevation range, classification, intensity, color, or on many other point cloud characteristics.

Addressing a major concern among lidar users, Global Mapper Lidar Module provides an efficient and effective way to remove noise from point cloud data. This powerful filtering tool can reclassify or automatically delete any points that are beyond a prescribed elevation or height above the ground threshold within a local area.

The Lidar Module includes numerous tools for querying points based on both the point cloud attributes and on their geographic distribution. The Search function can be used to create a multi-level query of point classification, elevation range, intensity, or any of the other variables. Spatial querying options include identifying points contained within a selected polygon or points that are within a defined distance of a certain type of point type or line feature. This function is ideally suited for encroachment detection.

Using a set of customizable parameters, this tool reduces the number of points in a point cloud resulting in a more manageable file size while eliminating redundancy. This thinning process can be applied consistently across the 2D extent of the layer or it can vary to reflect the 3D distribution of points.

One of the Lidar Module’s most powerful capabilities, the feature extraction tool is used to create a vector (point, line, or polygon) features derived from appropriately classified points. Based on a series of customizable settings, patterns of points representing buildings, trees, poles, and utility cables are analyzed and their extent is automatically delineated as a series of 3D vector objects or, in the case of buildings, as a 3D mesh.

Using the Perpendicular Path Profile function, a series of custom spaced cross-sectional views are created perpendicular to a defined path through a point cloud. 3D vertices can be quickly and accurately placed at regular intervals within each successive profile view. When the sequence is complete, either a 3D linear or area feature is created using Global Mapper’s standard Digitizing tool. This is an ideal tool for delineating curbs, utility cables, pipelines, drainage ditches, or building rooflines from high-resolution point cloud data.

The display of points can be adjusted to reflect many of the attributes within the point cloud, including:

• Elevation
• Intensity
• Classification
• Return Number
• Height Above Ground
• Point Source ID
• NDVI/NDWI (when NIR attribute is present)
• Point Density

When overlaid on a raster or gridded layer, the RGB or NIR value from each underlying pixel can be added to the associated point.

The Lidar Module provides numerous options for creating a surface model. Supplementing the simple triangulation (TIN) process, binning offers a more efficient and customizable way to create DTM or DSM of a specific resolution. Hydro-flattening allows the inherent elevation values associated with 3D vector lines or polygons to override the point-based elevations when modeling water bodies or streams.

The module enables the display of a section of a point cloud in the Path Profile view. This lateral perspective is initially created by establishing a swath width to ensure that sufficient points are displayed for the area of interest. The lateral perspective is ideally suited for manual point selection and editing as it clearly distinguishes points that are vertically offset from those in the surrounding area.

• E57 lidar format support
• Leica PTS point cloud file support