Introduction

License: BSD-3 Documentation Status

The pchandler module provides a comprehensive set of tools for handling, manipulating, and analyzing 3D point cloud data. Its components are modularly designed, covering geometry processing, field-of-view (FoV) management, data input/output, and utility functions. The package is optimized for flexibility, efficiency, and extensibility, supporting both CPU and GPU acceleration for scalable workflows.

Components:

  1. pchandler.core

Contains the main PointCloudData object for working with point clouds and interfaces with the other components.

  1. pchandler.geometry

  • Core coordinaate classes from which PointCloudData inherits from the CartesianCoordinates

  • Numerical optimal shift functionality to enable more efficient processing with float 32 data types

  • Splitter functions to separate the point cloud based on geometry

  • (Under Development) Easy 3D Transformation classes and handling to track coordinate system transformations or scan registrations

import numpy as np
from pchandler import PointCloudData
PointCloudData.__bases__[0]
# -> (<class 'CartesianCoordinates'>)

Furthermore, it has addition dedicated spherical coordinate based classes for managing angular regions in 3D space:

  • FoV and FoVTree for working with Field-of-Views from the scan origin

  • Angle and AngleArray for a flexible class for simple handling of various angular units

from pchandler.geometry.spherical.fov import FoV
from pchandler.geometry.spherical.angle import Angle

# Define a field of view
fov = FoV(left=Angle(0, 'deg'), right=Angle(3.14, 'rad'), top=Angle(0, 'gon'), bottom=np.pi)

# Split the FoV into quadrants
quadrants = fov.quadrants()

  1. pchandler.data_io:

  • Provides utilities for reading and writing point cloud data in various formats, including PLY, LAS/LAZ, CSV and minor E57 support.

  • Includes support for managing colors, normals, and scalar fields during import/export.

from pchandler.data_io import Ply
from pathlib import Path

# Load a PLY file
pcd = Ply.load(Path("example.ply"))

# Save the point cloud to another file
Ply.save(pcd, Path("output.ply"))

A generic file loader also exists that automatically attempts to load the file based on the file extension:

from pchandler import load_file
pcd = load_file("PointCloud.e57")

  1. pchandler.filters: Provides the following types of filters. - Includes the AngleUnit enum and a robust convert_angles function.

from pchandler.util import convert_angles, AngleUnit
import numpy as np

# Convert angles from degrees to radians
degrees = np.array([0, 45, 90, 180])
radians = convert_angles(degrees, source_unit=AngleUnit.DEGREE, target_unit=AngleUnit.RAD)
print("Radians:", radians)

Key Features:

  1. Point Cloud Geometry: - Manage and manipulate point cloud data with attributes like coordinates, colors, normals, and scalar fields. - Support for operations such as filtering, voxel downsampling, and outlier removal.

  2. Field-of-View (FoV) Management: - Define and manipulate rectangular angular regions (FoVs) in 3D space. - Hierarchical FoV trees for spatial partitioning and efficient data processing. - Functions for splitting, merging, tiling, and converting between angular units.

  3. Data I/O: - Read and write various point cloud file formats, including PLY, LAS/LAZ, and ASCII. - Efficient handling of large datasets with GPU acceleration (when supported).

Dependencies:

  • Core Libraries: - numpy: For numerical computations and array manipulation. - geopandas, shapely: For geometry processing and spatial operations. - alphashape: For generating alpha shapes to compute point cloud outlines.

  • Optional GPU Support: - cudf, cuspatial, cuml: For accelerated processing of point cloud data. - Requires a CUDA-enabled GPU for GPU-based functionalities.

  • Point Cloud I/O: - plyfile: For reading/writing PLY files. - laspy: For handling LAS/LAZ file formats.

  • Other Utilities: - joblib: For parallel processing. - open3d: For interfacing with the Open3D library for visualization and advanced operations.

This modular design ensures that pchandler is both extensible and scalable, making it suitable for a wide range of applications in 3D data analysis, GIS, and computer vision.

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