Visualizing lidar data using Uber Autonomous Visualization System (AVS) and Jupyter Notebook Application
Jupyter Notebook-based workflows for programmatically accessing, processing, and visualizing 3D Elevation Program (3DEP) lidar data
A series of jupyter notebook pipelines for processing lidar point clouds (LAS files) and deriving vegetation structure metrics.
This notebook uses a Voxel subsampling method for point cloud data thinning. After the point cloud has been thinned, triangulation is computed to create a mesh which can be exported as a STL file and opened in a variety of 3D modeling software.
Semantic segmentation of LIDAR point clouds from the KITTI-360 dataset using a modified PointNet2. This is a Python and PyTorch based implementation using Jupyter Notebooks.
Some ipython notebooks, to display and explore 3D LIDAR data. Also an interactive binder version.
A Jupyter notebook that demonstrates a Python™ implementation of NASA's Airborne Topographic Mapper (ATM) centroid tracker and compares it with results from the equivalent MATLAB® function.
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