probreg is a Python library designed for robust point cloud registration, implementing a suite of probabilistic algorithms including Coherent Point Drift (CPD), FilterReg, Bayesian CPD (BCPD), and GMMTree. It serves as a specialized tool for computer vision and robotics applications requiring the alignment of 3D point sets under rigid, affine, or non-rigid transformations. The library provides a clean Python API that abstracts complex mathematical operations, allowing users to register point clouds with support for both CPU (OpenMP) and GPU (CuPy) acceleration. Architecturally, the project follows a hybrid model where performance-critical kernels are implemented in C++ and exposed to Python via pybind11. This ensures high computational efficiency for operations like the Kabsch algorithm, point-to-plane distance calculations, and permutohedral lattice filtering. The Python layer manages the orchestration of these kernels, handling data I/O (via Open3D), transformation modeling, and iterative optimization loops. The codebase is well-structured with distinct modules for cost functions, transformations, and specific registration algorithms, promoting modularity and extensibility. The repository demonstrates strong engineering practices with a comprehensive test suite covering core algorithms, CI/CD pipelines for cross-platform builds (Ubuntu/Windows), and Sphinx documentation. It includes example scripts that serve as both documentation and validation for the library's functionality. The inclusion of C++ extensions and dependency on Eigen and pybind11 indicates a mature build system capable of handling complex native compilation requirements. The project is actively maintained with recent commits and a stable versioning strategy.
PiLiDAR is a DIY 360° 3D panorama scanner application for Raspberry Pi. It integrates a LiDAR (LD06/STL27L), stepper motor, IMU, and Raspberry Pi camera to capture 2D scans and photos, then processes them into colored 3D point clouds and 360° panoramas. The system is orchestrated by a main Python script (PiLiDAR.py) that controls hardware, captures data, and runs post-processing pipelines. The codebase is modular with a library of device drivers, calibration utilities, and 3D processing functions. It supports hardware PWM via curve-fitted speed control, automatic exposure and white balance calibration, Hugin-based panorama stitching, and Open3D-based point cloud registration, meshing, and visualization. The architecture is monolithic but well-organized, with clear separation of concerns. Key strengths include robust LiDAR protocol handling, reusable stepper motor control, and a comprehensive point cloud processing pipeline. The project is actively maintained with recent commits and low technical drift, though it requires specific hardware and external tools (Hugin, rpicam-still) to function.