论文阅读 KPConv: Flexible and Deformable Convolution for Point Clouds
该文发表于ICCV2019，二作是PointNet、PointNet++的作者Charles R. Qi。该文提出专门用于点云数据处理的卷积算法KPConv（Kernel Point Convolution），可用于点云分类和语义分割问题，在多个benchmark数据集上均能取得state of the art的效果，甚至在几个数据集上超过第二名7-8个百分点 下载论文点这里 KPConv: Fl
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相关文章
- 1. 论文阅读：KPConv: Flexible and Deformable Convolution for Point Clouds
- 2. KPConv: Flexible and Deformable Convolution for Point Clouds
- 3. 学习论文 KPConv: Flexible and Deformable Convolution for Point Clouds
- 4. 【论文阅读】【综述】Deep Learning for 3D Point Clouds: A Survey
- 5. 论文阅读：Permutation Matters: Anisotropic Convolutional Layer for Learning on Point Clouds
- 6. 论文阅读：Driving on Point Clouds
- 7. 论文阅读：RepPoints: Point Set Representation for Object Detection
- 8. 论文阅读：Deformable ConvNets v2
- 9. 论文阅读之：Associatively Segmenting Instances and Semantics in Point Clouds
- 10. 【论文阅读】SSN: Shape Signature Networks for Multi-class Object Detection from Point Clouds