KennethKwabenaKenney/ptcfextract: ptcfextract v0.1.0

This is the first public release of ptcfextract v0.1.0, a Python tool and library for computing per-point local geometric features from LiDAR point clouds stored in LAS/LAZ format. 🚀 Key features Computes linearity, planarity, and scattering (L/P/S) per point using local covariance eigenanalysis Robust to sparse neighborhoods, degenerate covariance, and numerical instability Optional voxel downsampling for large point clouds Optional geometric outlier removal (SOR / ROR) Optional Open3D visualization: RGB = [Linearity, Planarity, Scattering] or single-feature colormap views Two export modes (both preserve original point information): Overwrite RGB with normalized L/P/S for immediate visualization Preserve original RGB and add ExtraBytes scalar fields (linearity, planarity, scattering) 🧩 Flexible usage Command-line interface for end-to-end processing Python API for feature-only extraction, allowing users to integrate with their own downsampling, visualization, or machine-learning pipelines 📦 Outputs Fully compatible with CloudCompare and other LAS/LAZ viewers Extra scalar fields are written as float32 ExtraBytes when enabled 📚 Citation A Zenodo DOI is automatically generated for this release. If you use ptcfextract in academic or professional work, please cite the corresponding Zenodo record. ⚠️ Notes Neighborhood radius and voxel size are specified in the same units as the input point cloud coordinates When overwriting RGB for datasets without native RGB fields, the point format is upgraded and a warning is issued This release establishes the stable v0.1.0 feature set and provides a foundation for future enhancements.