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PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space論文解讀
時間 2020-12-30
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PointNet++ 核心問題:點雲的分類/語義分割 1、網絡構成 PointNet提取特徵的方式是對所有點雲數據提取了一個全局的特徵(見上圖),顯然,這和目前流行的CNN逐層提取局部特徵的方式不一樣。受到CNN的啓發,作者提出了PointNet++,它能夠在不同尺度提取局部特徵,通過多層網絡結構得到深層特徵。PointNet++由以下幾個關鍵部分構成: 1.1採樣層(sampling) 激光
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相關文章
1.
2020.10.20讀 PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
2.
《PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space》剖析筆記
3.
論文閱讀:PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
4.
[Point Cloud] PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation 論文解讀
5.
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2020.09.27讀 PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
7.
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation解析(PointNet模型)
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【論文閱讀筆記】PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
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