Learn how to bring point clouds data into Vizible too.
This section describes the workflow for adding point cloud content to Vizible.
Point clouds are a set of data points in a coordinate system. In 3D applications this coordinate system is designated by X, Y, and Z coordinates and shows the external surface of an object represented as individual points. Point clouds are created using 3D scanners, such as DotProduct, Faro, Matterport, and many others.
It is incredibly easy to incorporate VR into existing point-cloud workflows. Companies like DotProduct offer handheld scanning devices, like DotProduct’s DPI-8, which requires only minutes to fully scan an environment. It exports these 3D scans in .dp format and can be stored in the cloud immediately for remote access. These .dp files, as well as .ply, can be imported into Vizible and viewed in whatever VR hardware setup you have available to you at the exact scale that the environments exist in physical reality. If the particular task at hand calls for your point clouds to be meshed into surfaced 3D models, you can convert those files using one of many third party programs, like CloudCompare, which is free to use. These programs can export the resulting meshed 3D models in any of several file types that Vizible can also render in real time.
In many respects Vizible treats point clouds in the same way that it treats model files. There are several practical differences however. Point clouds occasionally do not interact nicely with some of the preset tools such as the laser pointer, as there is no solid surface with which they can intersect.
Vizible supports a few point cloud and scanned data file types including: .dp, .ply
There are several tools for converting existing point clouds to work well with Vizible. One free piece of software which has worked well is CloudCompare. This software allows users to convert most model formats into the ply format.
One of the key issues when dealing with point clouds is obtaining a point cloud model which is small enough to render quickly, but which also provides a reasonable representation of the captured scene or model. There are a great number of algorithms for providing filtering for point clouds, most of these algorithms do a good job of removing points from overly dense areas without impacting the final quality. CloudCompare offers some of these filtering algorithms.