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A novel network for classification of cuneiform tablet metadata

arXiv 2603.03892 ↗First posted 4 Mar 2026cs.CV cs.AIcited by 0

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Abstract (arXiv)

In this paper, we present a network structure for classifying metadata of cuneiform tablets. The problem is of practical importance, as the size of the existing corpus far exceeds the number of experts available to analyze it. But the task is made difficult by the combination of limited annotated datasets and the high-resolution point-cloud representation of each tablet. To address this, we develop a convolution-inspired architecture that gradually down-scales the point cloud while integrating local neighbor information. The final down-scaled point cloud is then processed by computing neighbors in the feature space to include global information. Our method is compared with the state-of-the-art transformer-based network Point-BERT, and consistently obtains the best performance. Source code and data available at github.com/fhagelskjaer/cuneiform3d

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 14 of its 24 references that are in the map. First posted on arXiv 4 Mar 2026.

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References · 14

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  2. 2023BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Modelscited by 6,137
  3. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,248
  4. 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,401
  5. 2018Dynamic Graph CNN for Learning on Point Cloudscited by 2,223
  6. 2017VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detectioncited by 1,323
  7. 2021Point-BERT: Pre-training 3D Point Cloud Transformers with Masked Point Modelingcited by 653
  8. 2019RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Cloudscited by 585
  9. 2023PointLLM: Empowering Large Language Models to Understand Point Cloudscited by 333
  10. 2023ULIP-2: Towards Scalable Multimodal Pre-training for 3D Understandingcited by 202
  11. 2022Deep learning classification of large-scale point clouds: A case study on cuneiform tabletscited by 7
  12. 2023CNN based Cuneiform Sign Detection Learned from Annotated 3D Renderings and Mapped Photographs with Illumination Augmentationcited by 6
2 more
  1. 2024Shaping History: Advanced Machine Learning Techniques for the Analysis and Dating of Cuneiform Tablets over Three Millenniacited by 2
  2. 2025ArrowPose: Segmentation, Detection, and 5 DoF Pose Estimation Network for Colorless Point Cloudscited by 2

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