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.
Following the interactive tutorials helps you to learn basic preprocessing tasks.
- Rotation of 3D point sets in python
- Similarity transformation estimation
- Iterative Clostest Point (ICP)
- Project Points in 2D Raster
- Principal Component Analysis (PCA)
- Random Sample Consensus (RANSAC)
- RANSAC Optimization & Depth Image to Point Cloud