Clouds are the most basic of the three large dataset objects supported by Carlson Point Cloud. Clouds always consist of a set of positions and can also contain color, intensity, and normals associated with each point. Point Cloud has several functions that can help reduce, clean, and generally prepare Clouds to be manageable in a CAD application. In the project's tree structure, clouds are categorized under Processed Data in their own folder, Clouds. In addition to the general actions common to most of the other data elements with 3D data, such as view, Clouds have a couple of unique functions that make them distinct from other data objects.
To import a cloud from a file bring up the right-click menu for the Clouds folder and select import. This will bring up the list of supported manufacturers.
| Manufacturer | Extension | Carlson | Pointools | 3DTK | Format | Color/Intensity Support |
|---|---|---|---|---|---|---|
| ASCII | txt, asc, nez, dat, csv | Yes | Yes | Yes | ASCII | Yes |
| ASPRS | las | Yes | Yes | Yes | Binary | Yes |
| ASTM | E57 | Yes | Yes | Yes | Binary | Yes |
| DEM/ESRI | dem, adf, asc | Yes | Yes | No | Carlson | Yes |
| Faro | fls | Yes | Yes | Yes | Binary | |
| Faro | fws | Yes | Yes | Yes | Workspace binary format | |
| LASzip.org | laz | Yes | Yes | Yes | Compressed version of LAS file(s) | Yes |
| Leica | ptg | No | Yes | No | ASCII or binary, Project or scar | Yes |
| Leica | pts | No | Yes | No | ASCII Format | Yes |
| Leica | ptx | No | Yes | No | ASCII Format | Yes |
| Riegl | 3dd | No | Yes | No | Binary | |
| Riegl | rdb | No | Yes | No | Riegl database format | |
| Riegl | rsp | No | Yes | No | XML based project file | |
| Stanford University | PLY | Yes | Yes | Yes | ASCII or Binary | Yes |
| TerraScan | bin | No | Yes | No | ||
| Topcon | cl3 | No | Yes | No | Binary |
Selecting one of the manufacturers will open the standard Carlson file selection dialog and after the file has been selected the file is imported. If an ASCII format is selected the Import Cloud dialog will be displayed.
Here, the user must define what delimiter was used for the ASCII file, if the data not organized properly in columns (all the data is in one column for example); the delimiter will probably need to be changed. Clicking the header of each column will bring up a menu with all the possible values that can be assigned to it, click each column's header to set its proper value. At the top of the dialog is the Presets Panel, which allows you to save the current column assignments to be used in the future. The import column is used to determine whether to import that particular column (often files will have text or header information that isn't actually part of the data set). Save the current configuration by clicking the + button in the panel, presets can also be removed by clicking - button. Presets are saved in the global program settings so they can be used across multiple projects.
There are several methods for creating a cloud, but all of these methods use one of two cloud creation dialogs. The most common and direct method of creating a cloud is to right-click the source data object (either another cloud, a mesh or a scan) and select Create ⇒ Cloud. This dialog operates in two different modes: multiple source mode and single source mode.
Single Source mode is typically the result of right-clicking a data object in the Point Cloud tree structure and selecting Create Cloud, this mode only allows the cloud to have the object that was selected as its source.Multiple Source mode is only accessible by right-clicking the Clouds folder itself and selecting Add ⇒ New in the menu. In this mode, the user will see a tree structure that represents the project on the left side of the dialog.
You can toggle inclusion of any object in the project by clicking the x icons next to them to turn them green. The right side of the dialog works the same as in single source mode.
Right-click a cloud and select Clean to open the Clean Cloud dialog. If you are viewing a cloud in a scene, select the part of the cloud to clean using the options in the Selection Set panel of the Action Tab, then select Clean from the Edit panel. The Clean Cloud command launched when viewing a scene will only use the Redundant Points method.
The Clean function will attempt to clean up the data in a cloud through three different methods, Duplicate Points and Isolate Points may be used individually or in combination. To use Redundant Points the first two options must be disabled.
Remove Duplicated Points method will search for any places where two or more points in the cloud are within the Distance threshold of each other. If any such points are found, they are deleted from the cloud. This method is to help remove redundant data that for all intents and purposes are the same given the current data set (say a data set that spans several miles having two points within inches of each other) and reduce the size of the data set.
Remove Isolated Points method will search for any points in the cloud that have less than Minimum Neighbors Count points within Distance Threshold units from them and delete them. This will remove points that are likely to be outliers, which could be a result of bad data (such as the scanner hitting a dust particle several meters off the ground). After clicking the green check button, the cloud cleaning process will begin. The time required to clean a dataset varies depending on the size of the dataset.
Redundant Points method uses a series of user parameters to clean points from the cloud. Using a box of user defined size points are checked for fitting the plane and/or color match. If no points match the box is subdivided and the check repeated. These sub-boxes will again be subdivided if no points match. If points are found that meet the criteria a point closest to the average of those in the box is copied to the clean cloud. If no match is found, all the points from that box are copied into the clean cloud unless Strong Filtration is on.
- Box Length and Width Sets the horizontal distance to search for points to compare.
- Box height Sets the vertical component completing a 3D cube search area for points to compare.
- Residual tolerance is the measure of how far from a best fit plane points can be. A typical value would be 1/10 of the smallest box dimension.
- Color variance determines how much the color of a point can vary. When set to 255 color variance is not used.
- Recursion depth is the number of times we initial box is subdivided to find points that meet the parameters. Typical values used would be 3 or 4.
- Strong Filtration when on copies a point closest to the average for plane and/or color even if no plane or color match was established.
Point Cloud has two methods of cloud reduction. There is a fast, naive method, and a slower, more intelligent method. Right-clicking a cloud and selecting Resample will bring up the Resample Cloud dialog.
Step method is much faster at the cost of being less intelligent. It will simply reduce the cloud to 1 / Step its current size by only keeping one out of every nth vertices. So in the case of a step size of four, it will traverse the cloud and only keep every fourth vertex, deleting three for each one it keeps.
OC-Tree tree method is much more intelligent, but it can have significantly longer run times than the step method. It divides the bounding box of the cloud into blocks defined by the resolution size and then filters out points based on the minimum and maximum parameters. If a block has less than the minimum number of points, its contents will be deleted, if a block has more than maximum points, random points within that block will be removed until it has maximum points inside it.
Tab Location(s): Project
Tree Folder: Clouds
Prerequisite: Existing Cloud, Mesh, Scan data or ASCII
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