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Merge pull request #477 from isl-org/ssheorey/docs-fix
Fix RandLANet docs
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ml3d/tf/models/randlanet.py

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

ml3d/torch/models/randlanet.py

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

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