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

Deep learning classification of large-scale point clouds: A case study on cuneiform tablets