论文标题
大规模点云的深度学习分类：关于楔形文字的案例研究
Deep learning classification of large-scale point clouds: A case study on cuneiform tablets
论文作者
论文摘要
本文介绍了一种新颖的网络体系结构，用于大规模点云的分类。该网络用于对楔形文字片剂进行分类。由于超过一百万平板电脑仍未得到处理，这可以帮助创建平板电脑的概述。该网络在比较数据集上测试并获得最先进的性能。我们还介绍了新的元数据分类任务，网络在其中显示出令人鼓舞的结果。最后，我们介绍了新颖的最大注意力可视化，表明训练有素的网络侧重于预期的功能。可在https://github.com/fhagelskjaer/dlc-cuneiform上找到代码
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
