Picture 1 of 1

Deep Learning for 3D Point Clouds by Ge Li and Wei Gao (2024, Hardcover)

grandeagleretail (947797)

98.2% positive feedback

Price:

$314.13

Free shipping

Estimated delivery Wed, Sep 10 - Thu, Sep 18

Returns:

30 days returns. Buyer pays for return shipping. If you use an eBay shipping label, it will be deducted from your refund amount.

Condition:

Brand New

Deep Learning for 3D Point Clouds by Wei Gao, Ge Li. Author Wei Gao, Ge Li. Human and machine perception can be benefited from the success of using deep learning approaches, which can significantly improve 3D perception modeling and optimization, as well as 3D pre-trained and large models.

About this product

Product Identifiers

PublisherSpringer

ISBN-109819795699

ISBN-139789819795697

eBay Product ID (ePID)15071782619

Product Key Features

Number of PagesXvi, 322 Pages

Publication NameDeep Learning for 3d Point Clouds

LanguageEnglish

SubjectComputer Graphics, Computer Vision & Pattern Recognition, Virtual Worlds

Publication Year2024

TypeTextbook

Subject AreaComputers

AuthorGe Li, Wei Gao

FormatHardcover

Dimensions

Item Length9.3 in

Item Width6.1 in

Additional Product Features

Dewey Edition23

Number of Volumes1 vol.

IllustratedYes

Dewey Decimal006.37

Table Of ContentChapter 1. Introduction to 3D Point Clouds: Datasets and Perception.- Chapter 2. Learning Basics for 3D Point Clouds.- Chapter 3. Deep Learning-based Point Cloud Enhancement I.- Chapter 4. Deep Learning-based Point Cloud Enhancement II.- Chapter 5. Deep Learning-based Point Cloud Analysis I.- Chapter 6. Deep Learning-based Point Cloud Analysis II.- Chapter 7. Point Cloud Pre-trained Models and Large Models.- Chapter 8. Point Cloud-Language Multi-modal Learning.- Chapter 9. Open Source Projects for 3D Point Clouds.- Chapter 10. Typical Engineering Applications of 3D Point Clouds.- Chapter 11. FutureWork on Deep Learning-based Point Cloud Technologies.

SynopsisAs an efficient 3D vision solution, point clouds have been widely applied into diverse engineering scenarios, including immersive media communication, autonomous driving, reverse engineering, robots, topography mapping, digital twin city, medical analysis, digital museum, etc. Thanks to the great developments of deep learning theories and methods, 3D point cloud technologies have undergone fast growth during the past few years, including diverse processing and understanding tasks. Human and machine perception can be benefited from the success of using deep learning approaches, which can significantly improve 3D perception modeling and optimization, as well as 3D pre-trained and large models. This book delves into these research frontiers of deep learning-based point cloud technologies. The subject of this book focuses on diverse intelligent processing technologies for the fast-growing 3D point cloud applications, especially using deep learning-based approaches. The deep learning-based enhancement and analysis methods are elaborated in detail, as well as the pre-trained and large models with 3D point clouds. This book carefully presents and discusses the newest progresses in the field of deep learning-based point cloud technologies, including basic concepts, fundamental background knowledge, enhancement, analysis, 3D pre-trained and large models, multi-modal learning, open source projects, engineering applications, and future prospects. Readers can systematically learn the knowledge and the latest developments in the field of deep learning-based point cloud technologies. This book provides vivid illustrations and examples, and the intelligent processing methods for 3D point clouds. Readers can be equipped with an in-depth understanding of the latest advancements of this rapidly developing research field.

LC Classification NumberTA1634