Allows to match scans on the field while scanning. Saves a lot of time.
Recommend to use an iPad instead of an Android device. Response and transfer time was much better for an iPad device. Works even for several high quality scans.
Matched scans can be transferred to the LCR directly from the device.
Static: PC
If scanned with sufficient density, the Auto Cloud option can be checked upon importing, which will automatically bind the scans together.
The precision of scan matching can be improved by editing the points used for cloud-to-cloud matching. These points can be removed from matching process but still can be used for visualisation later on. Explained here. Really useful if you have buildings and trees together (static and dynamic structures).
If having scans between outdoor/indoor environment, it is better to process outdoor and indoor separately. Then merge them back together. Meanwhile, cut off the visual points going through windows/mirrors because they will create ghost points.
There should be usually only one bundle. However, during the automatic bind process, there might be separate bundles created.
Select two scans and click Visual Alignment if not satisfied with the automatic binding process. Scans can be aligned in xy-axes and z-axis separately.
Recommend to clean most of the noise before the data export. Choose the Bundle Cloud mode and change the view of the camera to front/back/let/right/up/down position and cut off the noisy points with the selection tool. You cannot undo one step. You can only restore all deleted points for particular setup.
There is a new surface tool to cut off some anomalies, as humans, ghosts etc from final merge. The surface detection works quite well. Recommend to see the tutorial in the video guide at the end.
single point cloud (unstructured): can be decimated only with reduce cloud option providing average point spacing value. Useful to have equally distributed points in the scan, but misses the scanner position. Recommend to use 1mm option. Have to be exported separately.
separate setups (structured): can be decimated only with sub-sampling factor method, but contains the scanner position. Useful to gain complete information from scanning. The sub-sampling decimation does not have equally distributed data, recommend to use only to have images and scan origins.
Possible to open in both Meshlab and CloudCompare software.
Cloud Compare is more useful to load and visualize all data.
Meshlab does not support automatic normals creation and visualizing the scans origin and images.
Scan origins are not located in the actual scan origin.
Recommend to use the lowest decimation possible to have the most information. Exporting without decimation provides a huge file that has to be decimated anyway.
Meshlab is more useful to load these data. It allows automatic normals computing. Useful for further mesh creation. However, Meshlab cannot import the single .ptx file, hence it is important to check the separate files button in LCR before exporting.