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.
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.
![3D Point Cloud Analysis: Traditional, Deep Learning, and Explainable Machine Learning Methods](http://img.images-bn.com/static/redesign/srcs/images/grey-box.png?v11.10.4)
3D Point Cloud Analysis: Traditional, Deep Learning, and Explainable Machine Learning Methods
146![3D Point Cloud Analysis: Traditional, Deep Learning, and Explainable Machine Learning Methods](http://img.images-bn.com/static/redesign/srcs/images/grey-box.png?v11.10.4)
3D Point Cloud Analysis: Traditional, Deep Learning, and Explainable Machine Learning Methods
146Paperback(1st ed. 2021)
Product Details
ISBN-13: | 9783030891824 |
---|---|
Publisher: | Springer International Publishing |
Publication date: | 12/13/2021 |
Edition description: | 1st ed. 2021 |
Pages: | 146 |
Product dimensions: | 6.10(w) x 9.25(h) x (d) |