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 popular as represetnation of data. Point clouds can be generated by dense image matching (DIM), laser scanners or infrared sensors like microsoft kinect. Rich information can be derived from 3D point clouds but they although can be hard to handle. In this context we need to work with 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 to extract information from them.
Following interactive tutorials helps you to learn basic preprocessing tasks.
- Rotation of 3D point sets in python
- Similarity transformation estimation
- Iterative Clostest Point (ICP)