RandLA-Net - 大规模点云的高效语义分割的Tensorflow实现(CVPR 2020)-python源码

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RandLA-Net - 大规模点云的高效语义分割的Tensorflow实现(CVPR 2020) RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds (CVPR 2020) 这是 RandLA-Net (CVPR2020, Oralpresentation) 的官方实现,这是一种用于大规模 3D 点云语义分割的简单高效的神经架构。 技术细节请参考:RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds 胡庆勇、杨博*、谢林海、Stefano Rosa、郭玉兰、王志华、Niki Trigoni、Andrew Markham。 [Paper] [Video] [Blog] (1) Setup 此代码已在 Ubuntu 16.04 上使用 Python 3.5、Tensorflow 1.11、CUDA 9.0 和 cuDNN 7.4.1 进行测试。 克隆仓库 git clone --depth=1 http

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