Capture reality at scale. Working with LiDAR sensors, aerial surveys, and remote sensing data to produce accurate, analysis-ready 3D datasets of the real world.
The complete 101 guide to point cloud ingestion in Python. Probe LAS headers with laspy, resolve CRS and datums with pyproj, measure real spacing, and emit one normalized record.
3D Sensor Abstraction and Point Cloud Ingestion Guide Read More »
The complete 101 guide to LiDAR point cloud processing in Python. Read LAS files with laspy, index with an Open3D KDTree, filter ground with CSF, build a DTM.
LiDAR and Point Cloud Processing Complete Guide Read More »
By Dr. Florent Poux, PhD in 3D geospatial sciences | Author, 3D Data Science with Python (O’Reilly) | Published 18 January 2024 3D Point Cloud Vectorization for LiDAR City Models This hands-on approach is standalone and covers the process of LiDAR Vectorization. We then focus on City Model Automatic Generation (LoD 0) in 5 main […]
Vectorization of 3D Point Cloud for LiDAR City Models Read More »
By Dr. Florent Poux, PhD in 3D geospatial sciences | Author, 3D Data Science with Python (O’Reilly) | Published 9 December 2020 Scale AI released a new LiDAR point cloud dataset, and accelerate the growth of Autonomous Driving research. Point Cloud Data labelling Data labelling, also called data annotation/tagging/classification, is the process of tagging (i.e. […]
Free LiDAR point cloud for self-driving cars Read More »
