This book introduces the point cloud; its applications in industry, and
the most frequently used datasets. It mainly focuses on three computer
vision tasks -- point cloud classification, segmentation, and
registration -- which are fundamental to any point cloud-based system.
An overview of traditional point cloud processing methods helps readers
build background knowledge quickly, while the deep learning on point
clouds methods include comprehensive analysis of the breakthroughs from
the past few years. Brand-new explainable machine learning methods for
point cloud learning, which are lightweight and easy to train, are then
thoroughly introduced. Quantitative and qualitative performance
evaluations are provided. The comparison and analysis between the three
types of methods are given to help readers have a deeper understanding.
With the rich deep learning literature in 2D vision, a natural
inclination for 3D vision researchers is to develop deep learning
methods for point cloud processing. Deep learning on point clouds has
gained popularity since 2017, and the number of conference papers in
this area continue to increase. Unlike 2D images, point clouds do not
have a specific order, which makes point cloud processing by deep
learning quite challenging. In addition, due to the geometric nature of
point clouds, traditional methods are still widely used in industry.
Therefore, this book aims to make readers familiar with this area by
providing comprehensive overview of the traditional methods and the
state-of-the-art deep learning methods.
A major portion of this book focuses on explainable machine learning as
a different approach to deep learning. The explainable machine learning
methods offer a series of advantages over traditional methods and deep
learning methods. This is a main highlight and novelty of the book. By
tackling three research tasks -- 3D object recognition, segmentation,
and registration using our methodology -- readers will have a sense of
how to solve problems in a different way and can apply the frameworks to
other 3D computer vision tasks, thus give them inspiration for their own
future research.
Numerous experiments, analysis and comparisons on three 3D computer
vision tasks (object recognition, segmentation, detection and
registration) are provided so that readers can learn how to solve
difficult Computer Vision problems.
