【点云识别】Adaptive Hierarchical Down-Sampling for Point Cloud Classification(CVPR 2020)
Adaptive Hierarchical Down-Sampling for Point Cloud Classification 本文介绍一篇cvpr2020里面关于点云分类降采样的文章。 论文 没有开源代码 1. 问题 FPS的时间复杂度太高，类似Samplenet 的方法会产生新的点，随机采样无法保证重要的点被保留下来。所以本文提出了一种不产生新的点的采样方法。 2. 思想 整体思想非常简
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- 1. 【点云识别】Grid-GCN for Fast and Scalable Point Cloud Learning (CVPR 2020)
- 2. 【点云识别】PointContrast: Unsupervised Pre-training for 3D Point Cloud Understanding (ECCV 2020)
- 3. Hierarchical Point-Edge Interaction Network for Point Cloud Semantic Segmentation
- 4. 【点云识别】Weakly Supervised Semantic Point Cloud Segmentation: Towards 10x Fewer Labels(CVPR 2020)
- 5. 【点云识别】Feature-metric Registration: A Fast Semi-supervised Approach for Robust Point Cloud(CVPR 2020)
- 6. 3D 点云识别: Geometric Feedback Network for Point Cloud Classification
- 7. 【点云识别】PointGroup: Dual-Set Point Grouping for 3D Instance Segmentation (CVPR 2020)
- 8. [点云补全]-Point Cloud Completion by Skip-attention Network with Hierarchical Folding
- 9. 【点云识别】AdaCoSeg: Adaptive Shape Co-Segmentation with Group Consistency Loss (CVPR 2020 oral)
- 10. [点云识别]-SegGCN: Efficient 3D Point Cloud Segmentation with Fuzzy Spherical Kernel
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相关文章
- 1. 【点云识别】Grid-GCN for Fast and Scalable Point Cloud Learning (CVPR 2020)
- 2. 【点云识别】PointContrast: Unsupervised Pre-training for 3D Point Cloud Understanding (ECCV 2020)
- 3. Hierarchical Point-Edge Interaction Network for Point Cloud Semantic Segmentation
- 4. 【点云识别】Weakly Supervised Semantic Point Cloud Segmentation: Towards 10x Fewer Labels(CVPR 2020)
- 5. 【点云识别】Feature-metric Registration: A Fast Semi-supervised Approach for Robust Point Cloud(CVPR 2020)
- 6. 3D 点云识别: Geometric Feedback Network for Point Cloud Classification
- 7. 【点云识别】PointGroup: Dual-Set Point Grouping for 3D Instance Segmentation (CVPR 2020)
- 8. [点云补全]-Point Cloud Completion by Skip-attention Network with Hierarchical Folding
- 9. 【点云识别】AdaCoSeg: Adaptive Shape Co-Segmentation with Group Consistency Loss (CVPR 2020 oral)
- 10. [点云识别]-SegGCN: Efficient 3D Point Cloud Segmentation with Fuzzy Spherical Kernel