Computational, Label, and Data Efficiency in Deep Learning for Sparse 3D Data
Overview & Synopsis
Deep learning is widely applied to sparse 3D data to perform challenging tasks, e.g., 3D object detection and semantic segmentation. However, the high performance of deep learning comes with high costs, including computational costs and the effort to capture and label data. This work investigates and improves the efficiency of deep learning for sparse 3D data to overcome the obstacles to the further development of this technology.
Indexed from repository: OAPEN Library
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www.oapen.org)
Document Information
| Document Title | Computational, Label, and Data Efficiency in Deep Learning for Sparse 3D Data |
|---|---|
| Author / Organization | Li, Lanxiao |
| Subject & Discipline | Efficiency; 3D Data; Artificial Intelligence; Effizienz; 3D-Daten; Kรผnstliche Intelligenz; Deep Learning |
| Categories | Humanities & Social Sciences |
| Publication Year | 2024 |
| License / Rights | Open Access / Creative Commons |
| Resource Verification | Listed PDF (Verification Pending) |
| Original Host Website |
OAPEN Library
(www.oapen.org)
|
| File Format | application/pdf (Portable Document Format) |
| Language | EN |
| Catalog ID | DOC-150521 |
| Original Source Link | Visit Source Page on www.oapen.org » |