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Computational, Label, and Data Efficiency in Deep Learning for Sparse 3D Data

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 an...

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Glavni autor: Li, Lanxiao
Format: Online
Jezik:engleski
Izdano: KIT Scientific Publishing 2024
Teme:
Efficiency; 3D Data; Artificial Intelligence; Effizienz; 3D-Daten; Künstliche Intelligenz; Deep Learning
thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TH Energy technology and engineering::THR Electrical engineering
Online pristup:OCN: 1435579452
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Sažetak: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.

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