DEEP LEARNING CLASSIFICATION OF LARGE-SCALE POINT CLOUDS: A CASE STUDY ON CUNEIFORM TABLETS

被引：0

作者：

Hagelskjaer, Frederik ^{[1
]}

机构：

[1] Univ Southern Denmark, SDU Robot, Odense, Denmark

来源：

2022 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, ICIP | 2022年

关键词：

Deep learning; Point clouds; Classification; Cuneiform;

D O I：

10.1109/ICIP46576.2022.9898032

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper introduces a novel network architecture for the classification of large-scale point clouds. The network is used to classify metadata from cuneiform tablets. As more than half a million tablets remain unprocessed, this can help create an overview of the tablets. The network is tested on a comparison dataset and obtains state-of-the-art performance. We also introduce new metadata classification tasks on which the network shows promising results. Finally, we introduce the novel Maximum Attention visualization, demonstrating that the trained network focuses on the intended features. Code available at https://github.com/fhagelskjaer/dlc-cuneiform

引用

页码：826 / 830

页数：5

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