I will demonstrate the tools we use to process large scale point cloud datasets, and our interactive workflow which enables us to quickly fine-tune custom 3D modelling algorithms.
With the advent of many extensive openly acessible point cloud datasets (like Flanders' region-wide lidar dataset), processing those point clouds becomes increasingly challenging, requiering efficient, robust algorithms. While off-the-shelf algorithm exist for common tasks like ground/non-ground segmentation, advanced 3D modelling still remains mostly in the realm of tailor-made algorithms.
Starting from established processing tools like pdal, I'll show an interactive workflow to iteratively explore and develop custom 3D modelling algorithms, through the web-based jupyter interface and in particular the ipyvolume library.
Time allowing, I will discuss some work in progress for pdal, as well as upcoming tools in the jupyter ecosystem.
Speakers: Mathieu Carette