Reconstruction of high resolution 3D point cloud models based on Auto-encoder and Generative Adversarial Networks System
碩士 === 國立成功大學 === 工程科學系 === 106 === In this thesis, a 3D generative system which reconstructs the complete 3D structure of high resolution point clouds from sparse point clouds using an end-to-end autoencoder and generative adversarial networks is proposed. We present the deeplearning and data gener...
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| Format: | Others |
| Language: | zh-TW |
| Published: |
2018
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| Online Access: | http://ndltd.ncl.edu.tw/handle/agsa75 |
| Summary: | 碩士 === 國立成功大學 === 工程科學系 === 106 === In this thesis, a 3D generative system which reconstructs the complete 3D structure of high resolution point clouds from sparse point clouds using an end-to-end autoencoder and generative adversarial networks is proposed. We present the deeplearning and data generation process of the 3D generative system. The key idea of the system is to combine autoencoder and cycle generative adversarial networks framework.
The input sparse point clouds are derived from random sampling of a thousand of points in the ground truth point clouds. A paired training approach is used, which allows the system to learn the mapping between an input sparse point clouds and an output dense point clouds because the relationship between the data is very important. The goal is to translate the dense style that sparse point clouds learn into reconstructed high resolution dense point clouds.
Finally, the results prove that the network can translate sparse point clouds to dense distribution by CycleGANs training. The network it trained on single category can be applied to other untrained categories. The results have excellent scores in IoU metrics. In summary, the 3D generative system in this thesis can recover broken surface and details to reconstruct high resolution dense point clouds effectively with a thousand sparse point clouds.
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