Deep learning classification of large-scale point clouds: A case study on cuneiform tablets
This work addresses the challenge of processing over half a million unprocessed cuneiform tablets to create an overview, which is incremental as it applies a novel network architecture to a specific domain.
The paper tackles the problem of classifying metadata from large-scale point clouds of cuneiform tablets, achieving state-of-the-art performance on a comparison dataset and showing promising results on new classification tasks.
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