PointAugment: an Auto-Augmentation Framework for Point Cloud Classification 3/21
PointAugment:一种自动增强的点云分类框架 Abstract 与现有的自动2D图像的增强方法，PointAugment是感知样本并采取对抗学习策略共同优化增强器网络和分类器网络工作，以便增强器可以学习产生八筛选出最适合分类器的样本。 而且，制定一个可学习的点增强函数形状变换和点位移并仔细设计损失函数以采用增强型基于分类器学习进度的样本。 Overview in
相关文章
- 1. 04 PointAugment: an Auto-Augmentation Framework for Point Cloud Classification
- 2. A GRAPH-CNN FOR 3D POINT CLOUD CLASSIFICATION
- 3. 3D 点云识别: Geometric Feedback Network for Point Cloud Classification
- 4. Point Cloud 2019
- 5. [Point Cloud] PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation 论文解读
- 6. 【点云识别】Adaptive Hierarchical Down-Sampling for Point Cloud Classification(CVPR 2020)
- 7. 读论文 3D Point Cloud Classification using 3D Modified Fisher Vector Representation for CNN
- 8. Hierarchical Point-Edge Interaction Network for Point Cloud Semantic Segmentation
- 9. Deep Learning on Point Sets for 3D Classification and Segmentation
- 10. 【论文整理】Point Cloud Dataset
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相关文章
- 1. 04 PointAugment: an Auto-Augmentation Framework for Point Cloud Classification
- 2. A GRAPH-CNN FOR 3D POINT CLOUD CLASSIFICATION
- 3. 3D 点云识别: Geometric Feedback Network for Point Cloud Classification
- 4. Point Cloud 2019
- 5. [Point Cloud] PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation 论文解读
- 6. 【点云识别】Adaptive Hierarchical Down-Sampling for Point Cloud Classification(CVPR 2020)
- 7. 读论文 3D Point Cloud Classification using 3D Modified Fisher Vector Representation for CNN
- 8. Hierarchical Point-Edge Interaction Network for Point Cloud Semantic Segmentation
- 9. Deep Learning on Point Sets for 3D Classification and Segmentation
- 10. 【论文整理】Point Cloud Dataset