- Title
- Synthetic Data Generation and Deep Learning for the Topological Analysis of 3D Data
- Creator
- Peek, Dylan; Skerritt, Matthew P.; Chalup, Stephan
- Relation
- 2023 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2023. Proceedings of 2023 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2023 (Port Macquarie, Australia 28 November - 1 December 2023) p. 121-128
- Publisher Link
- http://dx.doi.org/10.1109/DICTA60407.2023.00025
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Resource Type
- conference paper
- Date
- 2023
- Description
- This research uses deep learning to estimate the topology of manifolds represented by sparse, unordered point cloud scenes in 3D. A new labelled dataset was synthesised to train neural networks and evaluate their ability to estimate the genus of these manifolds. This data used random homeomorphic deformations to provoke the learning of visual topological features. We demonstrate that deep learning models could extract these features and discuss some advantages over existing topological data analysis tools that are based on persistent homology. Semantic segmentation was used to provide additional geometric information in conjunction with topological labels. Common point cloud multi-layer perceptron and transformer networks were both used to compare the viability of these methods. The experimental results of this pilot study support the hypothesis that, with the aid of sophisticated synthetic data generation, neural networks can perform segmentation-based topological data analysis. While our study focused on simulated data, the accuracy achieved suggests a potential for future applications using real data.
- Subject
- persistent homology; deep learning; 3D pattern analysis; topological data analysis; semantic segmentation; synthetic data generation
- Identifier
- http://hdl.handle.net/1959.13/1503632
- Identifier
- uon:55364
- Identifier
- ISBN:9798350382204
- Language
- eng
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