From raw point clouds to real applications

This part defines point clouds, explains how LiDAR measures range and where its errors arise, and examines the acquisition platforms, georeferencing, file formats and spatial indexes that affect every later operation.

• 01Introduction to 3D Point Clouds Free
• 02LiDAR Principles and Technology Free
• 03LiDAR Acquisition Systems Free
• 04Data Formats and Spatial Indexing

Preprocessing prepares observations for analysis: sampling reduces the point count, filtering identifies unwanted returns and reduces noise, normal estimation describes local surface direction, and registration aligns datasets in a common frame.

• 05Sampling and Resampling
• 06Filtering and Denoising
• 07Normal Estimation and Orientation
• 08Registration

Analysis converts local geometry and attributes into useful descriptions by separating ground from other returns, estimating geometric features, grouping points into regions, assigning semantic classes and establishing correspondences between scans.

• 09Ground Extraction and DTM Generation
• 10Local Geometric Features
• 11Segmentation
• 12Classification
• 13Local Descriptors and Feature Matching

Reconstruction and change analysis derive geometric products from sampled observations by comparing survey epochs, fitting surfaces, reconstructing objects such as buildings and trees, and organising their geometry and meaning in semantic models.

• 14Change Detection
• 15Surface Reconstruction and Meshing
• 16Object Reconstruction
• 17Semantic 3D Models and Digital Twins

Applications such as national mapping, forestry, heritage, mining and autonomous driving determine which products are needed and how their quality should be assessed, and the final chapters examine deep networks, radiance fields and Gaussian splatting.

• 18Applications of 3D Point Cloud Processing Free
• 19Deep Learning for Point Clouds
• 20Radiance Fields and Gaussian Splatting

The appendix develops the mathematical tools used throughout the book: vectors and distances, rigid transformations, principal component analysis and least-squares estimation with uncertainty propagation.

• AMathematical Background Free