東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Deep learning for 3D point clouds

Gao, Wei.

Linked to FindBook

Google Book

Amazon

博客來

Deep learning for 3D point clouds

Record Type:	Electronic resources : Monograph/item
Title/Author:	Deep learning for 3D point clouds/ by Wei Gao, Ge Li.
Author:	Gao, Wei.
other author:	Li, Ge.
Published:	Singapore :Springer Nature Singapore : : 2025.,
Description:	xvi, 322 p. :ill. (chiefly color), digital ;24 cm.
[NT 15003449]:	Chapter 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.
Contained By:	Springer Nature eBook
Subject:	Three-dimensional imaging. -
Online resource:	https://doi.org/10.1007/978-981-97-9570-3
ISBN:	9789819795703

Deep learning for 3D point clouds
Gao, Wei.

Deep learning for 3D point clouds[electronic resource] /by Wei Gao, Ge Li. - Singapore :Springer Nature Singapore :2025. - xvi, 322 p. :ill. (chiefly color), digital ;24 cm.

Chapter 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.

As 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.

ISBN: 9789819795703

Standard No.: 10.1007/978-981-97-9570-3doiSubjects--Topical Terms:

589407
Three-dimensional imaging.

LC Class. No.: TA1560

Dewey Class. No.: 006.693

Deep learning for 3D point clouds
LDR:03465nmm a2200325 a 4500 001 2407771
003 DE-He213
005 20241207115232.0
006 m o d
007 cr nn 008maaau
008 260204s2025 si s 0 eng d
020 $a 9789819795703 $q (electronic bk.)
020 $a 9789819795697 $q (paper)
024 7 $a 10.1007/978-981-97-9570-3 $2 doi
035 $a 978-981-97-9570-3
040 $a GP $c GP
041 0 $a eng
050 4 $a TA1560
072 7 $a UYQV $2 bicssc
072 7 $a COM016000 $2 bisacsh
072 7 $a UYQV $2 thema
082 0 4 $a 006.693 $2 23
090 $a TA1560 $b .G211 2025
100 1 $a Gao, Wei. $3 1073960
245 1 0 $a Deep learning for 3D point clouds $h [electronic resource] / $c by Wei Gao, Ge Li.
260 $a Singapore : $b Springer Nature Singapore : $b Imprint: Springer, $c 2025.
300 $a xvi, 322 p. : $b ill. (chiefly color), digital ; $c 24 cm.
505 0 $a Chapter 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.
520 $a As 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.
650 0 $a Three-dimensional imaging. $3 589407
650 0 $a Deep learning (Machine learning) $3 3538509
650 1 4 $a Computer Vision. $3 3538524
650 2 4 $a Virtual and Augmented Reality. $3 3599064
650 2 4 $a Computer Graphics. $3 892532
650 2 4 $a Image Processing. $3 891209
650 2 4 $a Coding and Information Theory. $3 891252
700 1 $a Li, Ge. $3 3717087
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-981-97-9570-3
950 $a Computer Science (SpringerNature-11645)