論文閱讀 KPConv: Flexible and Deformable Convolution for Point Clouds
該文發表於ICCV2019，二作是PointNet、PointNet++的作者Charles R. Qi。該文提出專門用於點雲數據處理的卷積算法KPConv（Kernel Point Convolution），可用於點雲分類和語義分割問題，在多個benchmark數據集上均能取得state of the art的效果，甚至在幾個數據集上超過第二名7-8個百分點 下載論文點這裏 KPConv: Fl
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- 4. 【論文閱讀】【綜述】Deep Learning for 3D Point Clouds: A Survey
- 5. 論文閱讀：Permutation Matters: Anisotropic Convolutional Layer for Learning on Point Clouds
- 6. 論文閱讀：Driving on Point Clouds
- 7. 論文閱讀：RepPoints: Point Set Representation for Object Detection
- 8. 論文閱讀：Deformable ConvNets v2
- 9. 論文閱讀之：Associatively Segmenting Instances and Semantics in Point Clouds
- 10. 【論文閱讀】SSN: Shape Signature Networks for Multi-class Object Detection from Point Clouds
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相關文章
- 1. 論文閱讀：KPConv: Flexible and Deformable Convolution for Point Clouds
- 2. KPConv: Flexible and Deformable Convolution for Point Clouds
- 3. 學習論文 KPConv: Flexible and Deformable Convolution for Point Clouds
- 4. 【論文閱讀】【綜述】Deep Learning for 3D Point Clouds: A Survey
- 5. 論文閱讀：Permutation Matters: Anisotropic Convolutional Layer for Learning on Point Clouds
- 6. 論文閱讀：Driving on Point Clouds
- 7. 論文閱讀：RepPoints: Point Set Representation for Object Detection
- 8. 論文閱讀：Deformable ConvNets v2
- 9. 論文閱讀之：Associatively Segmenting Instances and Semantics in Point Clouds
- 10. 【論文閱讀】SSN: Shape Signature Networks for Multi-class Object Detection from Point Clouds