Deep Learning on Point Sets for 3D Classification and Segmentation
點雲數據是一種重要的幾何數據結構， 由於其不規則的格式， 大部分研究都集中在怎樣將數據轉換成3d立體網格. 但是這樣的工作是大量的且會出現問題. 所以作者提出一種新的網絡結構來處理這樣的情況，利用max_pooling來處理3D非有序點雲. Net Architect Classification NetWork 該網絡的輸入是 n \times 3n×3, 其中n是點雲的個數.
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- 2. CVPR2017_PointNet_ Deep Learning on Point Sets for 3D Classification and Segmentation
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- 5. PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation 論文筆記
- 6. 《PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation》點網解析
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- 8. 2020.09.27讀 PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
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相關文章
- 1. PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation解析(PointNet模型)
- 2. CVPR2017_PointNet_ Deep Learning on Point Sets for 3D Classification and Segmentation
- 3. PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
- 4. PointNet Deep Learning on Point Sets for 3D Classification and Segmentation
- 5. PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation 論文筆記
- 6. 《PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation》點網解析
- 7. 論文閱讀：PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
- 8. 2020.09.27讀 PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
- 9. 3D點雲網絡：PointNet:Deep Learning on Point Sets for 3D Classification and Segmentation
- 10. PointNet:Deep Learning on Point Sets for 3D Classificatiion and Segmentation