3D point cloud management & compression
This project aims to explore various data representations for Point Cloud, and algorithms for sparse Point Cloud. Referring to OctSqueeze[1] algorithm, an octree representation is used for Point Cloud which is able to retain required information as much as possible. Then the entropy model is appl...
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| 主要作者: | |
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| 其他作者: | |
| 格式: | Final Year Project |
| 語言: | English |
| 出版: |
Nanyang Technological University
2022
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| 主題: | |
| 在線閱讀: | https://hdl.handle.net/10356/157242 |
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| 總結: | This project aims to explore various data representations for Point Cloud, and algorithms for
sparse Point Cloud. Referring to OctSqueeze[1] algorithm, an octree representation is used
for Point Cloud which is able to retain required information as much as possible. Then the
entropy model is applied to further compress Point Cloud data octree leaf node to the
bitstream. The probability distribution for the entropy model is generated by a deep learning
model which takes context information available from the octree itself during training,
encoding, and decoding. In the ideal algorithm, the main optimization is that it concerns
three more context information in the deep learning model compared to OctSqueeze- the
tree node occupancy condition at the same level, the intensity information, and the context
information in the neighbor frame. Using OctSqueeze as the baseline, the project is trying
to optimize the Deep Entropy Model which uses Octree, deep learning, and entropy model
to compress the sparse Point Cloud sequential data from the KITTI dataset and try to
improve the metrics for accuracy & compression rate compared to the state-of-art algorithm
in the same fields. |
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