Tutorial : Point Cloud Data Processing

Afanche3D provides many useful features for 3D point cloud data processing. Point clouds can be acquired from hardware sensors such as stereo cameras, 3D scanners, LiDAR devices, or time-of-flight cameras, or generated from a computer program synthetically.

Support all popular 3D formats used for 3D point cloud

Afanche3D supports all popular 3D formats that are commonly used for 3D scanning, including ASC, PCD, CSV, XYZ, LAS, LAZ, OBJ, PLY, STL. Those formats are widely used by devices such as PrimSensor 3D cameras, the Microsoft Kinect, Intel RealSense cameras, or the Asus XTionPro. The following picture shows a 3D scanned model (in ASC format) in Afanche3D.

Measure 3D point cloud data

Afanche3D provides excellent measurement tools for you to measure 3D scanned model in any way you want. The following picture shows some measurements in Afanche3D:

Triangulate 3D point cloud

Select a 3D point cloud part, click Model menu, click Point Cloud, click Triangulation. The point cloud data will be triangulated into 3D mesh data. The following pictures show before and after triangulation operation:

Down sampling point cloud

Sometimes, the point cloud could contain too many points. That will slow down many operations. Down sampling point cloud will reduce the number of points.

To down sample point cloud, select a point cloud part first, then click Model menu, click Point Cloud, click Down Sampling.The following pictures show before and after down sampling operation:

Remove sparse outliers

Laser scans typically generate point cloud datasets of varying point densities. Additionally, measurement errors lead to sparse outliers which corrupt the results even more.

To remove sparse outliers, select a point cloud part first, then click Model menu, click Point Cloud, click Remove Outliers. The following pictures show before and after remove outliers operation:

Segmentation

Select a point cloud part first, then click Model menu, click Point Cloud, click Segmentation. The following pictures show before and after segmentation operation: