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11 | 11 |
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12 | 12 |
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13 | 13 | class RandLANet(BaseModel):
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14 |
| - """Class defining RandLANet, a Semantic Segmentation model. |
15 |
| - Based on the architecture |
16 |
| - https://arxiv.org/abs/1911.11236# |
| 14 | + """Class defining RandLANet, a Semantic Segmentation model. Based on the |
| 15 | + architecture from the paper `RandLA-Net: Efficient Semantic Segmentation of |
| 16 | + Large-Scale Point Clouds <https://arxiv.org/abs/1911.11236>`__. |
17 | 17 |
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18 |
| - Reference Implementation - https://github.com/QingyongHu/RandLA-Net |
| 18 | + RandLA-Net is an efficient and lightweight neural architecture which |
| 19 | + directly infer per-point semantics for large-scale point clouds. The key |
| 20 | + approach is to use random point sampling instead of more complex point |
| 21 | + selection approaches. Although remarkably computation and memory |
| 22 | + efficient, random sampling can discard key features by chance. To overcome |
| 23 | + this, we introduce a novel local feature aggregation module to |
| 24 | + progressively increase the receptive field for each 3D point, thereby |
| 25 | + effectively preserving geometric details. |
19 | 26 |
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20 |
| - RandLA-Net is an efficient and lightweight neural architecture which directly infer |
21 |
| - per-point semantics for large-scale point clouds. The key approach is to use random |
22 |
| - point sampling instead of more complex point selection approaches. Although |
23 |
| - remarkably computation and memory efficient, random sampling can discard key features |
24 |
| - by chance. To overcome this, we introduce a novel local feature aggregation module to |
25 |
| - progressively increase the receptive field for each 3D point, thereby effectively |
26 |
| - preserving geometric details. |
| 27 | + **Architecture** |
27 | 28 |
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28 |
| - Architecture |
29 | 29 | .. image:: https://user-images.githubusercontent.com/23613902/150006228-34fb9e04-76b6-4022-af08-c308da6dcaae.png
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| 30 | + :width: 100% |
30 | 31 |
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| 32 | + References: |
| 33 | + https://github.com/QingyongHu/RandLA-Net |
31 | 34 | """
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32 | 35 |
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33 | 36 | def __init__(
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