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

arXiv: https://arxiv.org/abs/2202.10851 · HTML: https://mlsanity.com/paper/2202.10851/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 19 references that are in the map.
- First posted on arXiv 22 Feb 2022.

## Builds on (most cited first, 8 of 8)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [Dynamic Graph CNN for Learning on Point Clouds](https://mlsanity.com/paper/1801.07829/) (2018)
- [Group Normalization](https://mlsanity.com/paper/1803.08494/) (2018)
- [Deep Learning for 3D Point Clouds: A Survey](https://mlsanity.com/paper/1912.12033/) (2019)
- [SplineCNN: Fast Geometric Deep Learning with Continuous B-Spline Kernels](https://mlsanity.com/paper/1711.08920/) (2017)
- [Bridging the Reality Gap for Pose Estimation Networks using Sensor-Based Domain Randomization](https://mlsanity.com/paper/2011.08517/) (2020)

## Built on by (most cited first, 7 of 7)

- [Ancient Script Image Recognition and Processing: A Review](https://mlsanity.com/paper/2506.19208/) (2025)
- [In-Hand Pose Estimation and Pin Inspection for Insertion of Through-Hole Components](https://mlsanity.com/paper/2208.01284/) (2022)
- [Shaping History: Advanced Machine Learning Techniques for the Analysis and Dating of Cuneiform Tablets over Three Millennia](https://mlsanity.com/paper/2406.04039/) (2024)
- [ArrowPose: Segmentation, Detection, and 5 DoF Pose Estimation Network for Colorless Point Clouds](https://mlsanity.com/paper/2506.08699/) (2025)
- [Advanced Deep Learning Approaches for Automated Recognition of Cuneiform Symbols](https://mlsanity.com/paper/2505.04678/) (2025)
- [A novel network for classification of cuneiform tablet metadata](https://mlsanity.com/paper/2603.03892/) (2026)
- [Zero-Shot SAM2 Segmentation and Vision Transformer-Based Recognition of Elamite Cuneiform Symbols from Degraded Tablet Images](https://mlsanity.com/paper/2608.18544/) (2026)

## Abstract (arXiv)

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