PointNet:Deep Learning on Point Sets for 3D Classificatiion and Segmentation
本文亮點： 直接把點雲數據作爲神經網絡的輸入，該網絡既可以用於分類也可以用於分割。 1，通過空間變換網絡解決旋轉不變問題。 2，利用maxpooling作爲對稱函數解決點雲無序問題。 一，點雲數據存在的問題 （1）無序性 點雲本質上是一長串點（nx3矩陣，其中n是點數）。在幾何上，點的順序不影響它在空間中對整體形狀的表示。因此，在提取點雲特徵時需要對無序數據做處理，使其對不同排列數據都有
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相關文章
- 1. Deep Learning on Point Sets for 3D Classification and Segmentation
- 2. 3D點雲網絡：PointNet:Deep Learning on Point Sets for 3D Classification and Segmentation
- 3. PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation解析(PointNet模型)
- 4. CVPR2017_PointNet_ Deep Learning on Point Sets for 3D Classification and Segmentation
- 5. PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
- 6. PointNet Deep Learning on Point Sets for 3D Classification and Segmentation
- 7. PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation 論文筆記
- 8. 《PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation》點網解析
- 9. 《PointNet：Deep Learning on Point Sets for 3D Classification and Segmentation》中文翻譯
- 10. 論文閱讀：PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation