3D Point Cloud Analysis: Traditional, Deep Learning, and Explainable Machine Learning Methods Hardback - 2021
by Shan Liu; Min Zhang; Pranav Kadam
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From the publisher
From the rear cover
With the rich deep learning literature in 2D vision, a natural inclination for 3D vision researchers is to develop deep learning methods for point cloudprocessing. 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.
Details
- Title 3D Point Cloud Analysis: Traditional, Deep Learning, and Explainable Machine Learning Methods
- Author Shan Liu; Min Zhang; Pranav Kadam
- Binding Hardback
- Pages 146
- Volumes 1
- Language ENG
- Publisher Springer
- Publication date 2021-12-11
- Illustrated Yes
- Features Illustrated
- ISBN 9783030891794 / 3030891798
- Weight 0.89 lbs (0.40 kg)
- Dimensions 9.21 x 6.14 x 0.44 in (23.39 x 15.60 x 1.12 cm)
- Category Computers - Other Applications
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3D Point Cloud Analysis : Traditional, Deep Learning, and Explainable Machine Learning Methods
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3D Point Cloud Analysis
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