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This book is organized into five sections, each of which addresses different aspects of 3D deep learning. Section I: Sample Efficient 3D Deep Learning, focuses on developing efficient algorithms to build accurate 3D models with limited annotated samples. Section II: Representation Efficient 3D Deep Learning, deals with the challenge of developing efficient representations for dynamic 3D scenes and multiple 3D modalities. Section III: Robust 3D Deep Learning, presents methods for improving the robustness and reliability of deep learning models in real-world applications. Section IV: Resource Efficient 3D Deep Learning, explores ways to reduce the computation cost of 3D models and improve their efficiency in resource-limited environments. Section V: Emerging 3D Deep Learning Applications, showcases how 3D deep learning is transforming industries and enabling new applications for healthcare and manufacturing.
This collection is a valuable resource for researchers and practitioners interested in exploring the potential of 3D deep learning.
This book is organized into five sections, each of which addresses different aspects of 3D deep learning. Section I: Sample Efficient 3D Deep Learning, focuses on developing efficient algorithms to build accurate 3D models with limited annotated samples. Section II: Representation Efficient 3D Deep Learning, deals with the challenge of developing efficient representations for dynamic 3D scenes and multiple 3D modalities. Section III: Robust 3D Deep Learning, presents methods for improving the robustness and reliability of deep learning models in real-world applications. Section IV: Resource Efficient 3D Deep Learning, explores ways to reduce the computation cost of 3D models and improve their efficiency in resource-limited environments. Section V: Emerging 3D Deep Learning Applications, showcases how 3D deep learning is transforming industries and enabling new applications for healthcare and manufacturing.
This collection is a valuable resource for researchers and practitioners interested in exploring the potential of 3D deep learning.
| Erscheinungsjahr: | 2024 |
|---|---|
| Genre: | Importe, Informatik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Buch |
| ISBN-13: | 9789811286483 |
| ISBN-10: | 9811286485 |
| Sprache: | Englisch |
| Einband: | Gebunden |
| Autor: | Xiaoli Li, Xulei Yang Hao Su |
| Hersteller: | World Scientific |
| Verantwortliche Person für die EU: | Libri GmbH, Europaallee 1, D-36244 Bad Hersfeld, gpsr@libri.de |
| Maße: | 235 x 157 x 31 mm |
| Von/Mit: | Xulei Yang Hao Su Xiaoli Li |
| Erscheinungsdatum: | 08.09.2024 |
| Gewicht: | 0,859 kg |
| Erscheinungsjahr: | 2024 |
|---|---|
| Genre: | Importe, Informatik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Buch |
| ISBN-13: | 9789811286483 |
| ISBN-10: | 9811286485 |
| Sprache: | Englisch |
| Einband: | Gebunden |
| Autor: | Xiaoli Li, Xulei Yang Hao Su |
| Hersteller: | World Scientific |
| Verantwortliche Person für die EU: | Libri GmbH, Europaallee 1, D-36244 Bad Hersfeld, gpsr@libri.de |
| Maße: | 235 x 157 x 31 mm |
| Von/Mit: | Xulei Yang Hao Su Xiaoli Li |
| Erscheinungsdatum: | 08.09.2024 |
| Gewicht: | 0,859 kg |
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