Compression Algorithms

Compression algorithms: These algorithms compress the point cloud data to reduce storage or transmission bandwidth.

The application of the LiDAR point cloud Compression algorithms

LiDAR point cloud compression algorithms are used to reduce the size of LiDAR data without significantly affecting its quality or accuracy. This is important because LiDAR data can be extremely large, and storing and transmitting it can be time-consuming and expensive. Compression algorithms can be used to reduce the storage and transmission requirements of LiDAR data while preserving its useful information. This is particularly useful in applications where LiDAR data needs to be transmitted over the internet or stored on portable devices with limited storage capacity. Additionally, LiDAR data compression can also reduce the computational cost of processing large datasets, enabling faster analysis and visualization of the data. Overall, LiDAR point cloud compression algorithms have numerous practical applications in industries such as surveying, forestry, mining, and transportation.

Here are the top 10 libraries for LiDAR point cloud compression algorithms:

1. LASzip: https://laszip.org/

LASzip is a free and open-source library for compressing LiDAR data in the LAS format. It provides lossless and lossy compression options and is widely used in the LiDAR community.

2. Entwine: https://entwine.io/

Entwine is a C++ library that provides tools for organizing, storing, and processing massive point cloud datasets. It includes a range of algorithms for point cloud compression, including lossless compression using the Zstandard algorithm.

3. Greyhound: https://greyhound.io/

Greyhound is a C++ library and server for streaming point cloud data over the web. It includes a range of algorithms for point cloud compression, including lossless compression using the Zstandard algorithm.

4. PCL (Point Cloud Library): https://pointclouds.org/

PCL is a C++ library that provides tools for processing and analyzing point cloud data. It includes a range of algorithms for point cloud compression, including lossless compression using the LZF algorithm.

5. CloudCompare: https://www.cloudcompare.org/

CloudCompare is a standalone application that provides tools for visualizing and processing point cloud data. It includes a range of algorithms for point cloud compression, including lossless compression using the Zstandard algorithm.

6. LASlib: https://github.com/LAStools/LASlib

LASlib is a C++ library for reading and writing LiDAR data in the LAS format. It includes support for lossless compression using the LZF algorithm.

7. LAStools: https://rapidlasso.com/lastools/

LAStools is a collection of command-line tools for processing LiDAR data. It includes a range of algorithms for point cloud compression, including lossless compression using the LZF algorithm.

8. libLAS: https://liblas.org/

libLAS is a C++ library that provides tools for reading, writing, and manipulating LiDAR data. It includes support for lossless compression using the LZF algorithm.

9. OpenVDB: https://www.openvdb.org/

OpenVDB is a C++ library for working with volumetric data, including point cloud data. It includes a range of algorithms for point cloud compression, including lossless compression using the Snappy algorithm.

10. FUSION/LDV: http://forsys.cfr.washington.edu/fusion/fusionlatest.html

FUSION is a software package for processing LiDAR data, developed by the USDA Forest Service. It includes support for lossless compression using the LZW algorithm.

Note that some of these libraries/tools are standalone software applications, while others are libraries that can be integrated with other software. Also, some of these libraries/tools are open source, while others are proprietary.