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Deep Learning for 3D Point Clouds

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 de...

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Bibliografski detalji
Glavni autori: Gao, Wei (Autor), Li, Ge (Autor)
Autor kompanije: SpringerLink (Online service)
Format: Elektronički e-knjiga
Jezik:engleski
Izdano: Singapore : Springer Nature Singapore : Imprint: Springer, 2025.
Izdanje:1st ed. 2025.
Teme:
Computer vision.
Virtual reality.
Augmented reality.
Computer graphics.
Image processing.
Coding theory.
Information theory.
Computer Vision.
Virtual and Augmented Reality.
Computer Graphics.
Image Processing.
Coding and Information Theory.
Online pristup:https://doi.org/10.1007/978-981-97-9570-3
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Sadržaj:
  • 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.

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