【點雲識別】Adaptive Hierarchical Down-Sampling for Point Cloud Classification(CVPR 2020)
Adaptive Hierarchical Down-Sampling for Point Cloud Classification 本文介紹一篇cvpr2020裏面關於點雲分類降採樣的文章。 論文 沒有開源代碼 1. 問題 FPS的時間複雜度太高，類似Samplenet 的方法會產生新的點，隨機採樣無法保證重要的點被保留下來。所以本文提出了一種不產生新的點的採樣方法。 2. 思想 整體思想非常簡
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- 1. 【點雲識別】Grid-GCN for Fast and Scalable Point Cloud Learning (CVPR 2020)
- 2. 【點雲識別】PointContrast: Unsupervised Pre-training for 3D Point Cloud Understanding (ECCV 2020)
- 3. Hierarchical Point-Edge Interaction Network for Point Cloud Semantic Segmentation
- 4. 【點雲識別】Weakly Supervised Semantic Point Cloud Segmentation: Towards 10x Fewer Labels(CVPR 2020)
- 5. 【點雲識別】Feature-metric Registration: A Fast Semi-supervised Approach for Robust Point Cloud(CVPR 2020)
- 6. 3D 點雲識別: Geometric Feedback Network for Point Cloud Classification
- 7. 【點雲識別】PointGroup: Dual-Set Point Grouping for 3D Instance Segmentation (CVPR 2020)
- 8. [點雲補全]-Point Cloud Completion by Skip-attention Network with Hierarchical Folding
- 9. 【點雲識別】AdaCoSeg: Adaptive Shape Co-Segmentation with Group Consistency Loss (CVPR 2020 oral)
- 10. [點雲識別]-SegGCN: Efficient 3D Point Cloud Segmentation with Fuzzy Spherical Kernel
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相關文章
- 1. 【點雲識別】Grid-GCN for Fast and Scalable Point Cloud Learning (CVPR 2020)
- 2. 【點雲識別】PointContrast: Unsupervised Pre-training for 3D Point Cloud Understanding (ECCV 2020)
- 3. Hierarchical Point-Edge Interaction Network for Point Cloud Semantic Segmentation
- 4. 【點雲識別】Weakly Supervised Semantic Point Cloud Segmentation: Towards 10x Fewer Labels(CVPR 2020)
- 5. 【點雲識別】Feature-metric Registration: A Fast Semi-supervised Approach for Robust Point Cloud(CVPR 2020)
- 6. 3D 點雲識別: Geometric Feedback Network for Point Cloud Classification
- 7. 【點雲識別】PointGroup: Dual-Set Point Grouping for 3D Instance Segmentation (CVPR 2020)
- 8. [點雲補全]-Point Cloud Completion by Skip-attention Network with Hierarchical Folding
- 9. 【點雲識別】AdaCoSeg: Adaptive Shape Co-Segmentation with Group Consistency Loss (CVPR 2020 oral)
- 10. [點雲識別]-SegGCN: Efficient 3D Point Cloud Segmentation with Fuzzy Spherical Kernel