(In progress)

3D Point Clouds

Information about our spatial environments is used in a large number of applications. When it comes to the gathering of 3D shape information point clouds are a popular way to represent data. Point clouds can be generated by dense image matching (DIM), laser scanners or infrared sensors like the microsoft kinect. Although rich information can be derived from 3D point clouds, that information can be hard to handle. In this context we need to work with a large amount of data, varying properties(i.e. density) and unclear spatial relations between different datasets. Here we learn how to handle point clouds and how to extract information from them.

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Preprocessing

Following the interactive tutorials helps you to learn basic preprocessing tasks.

Segmentation

Classification

Practical

If you search for an easy way to work the task on your machine, the easiest solution is to use our 3D point environment based on docker image. Here is a tutorial for docker newbies.

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