【點雲識別】PointGroup: Dual-Set Point Grouping for 3D Instance Segmentation (CVPR 2020)
PointGroup: Dual-Set Point Grouping for 3D Instance Segmentation 本文介紹一篇cvpr2020裏面關於點雲實例分割的文章。 論文 目前還沒有開源代碼 1. 問題 和2D圖片不同，3D點雲不存在遮擋現象，存在大量的void space， 本文想充分利用void space 進行分割。 2. 思想 那麼如何利用這些void space 呢
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相關文章
- 1. 【點雲識別】3D-MPA: Multi Proposal Aggregation for 3D Semantic Instance Segmentation (CVPR 2020)
- 2. 【點雲識別】Learning to Segment 3D Point Clouds in 2D Image Space (CVPR 2020)
- 3. CVPR 2020-Instance Segmentation（2020最新分割論文）
- 4. 【點雲識別】Weakly Supervised Semantic Point Cloud Segmentation: Towards 10x Fewer Labels(CVPR 2020)
- 5. [點雲識別]-SegGCN: Efficient 3D Point Cloud Segmentation with Fuzzy Spherical Kernel
- 6. 【點雲識別】PointContrast: Unsupervised Pre-training for 3D Point Cloud Understanding (ECCV 2020)
- 7. 【點雲識別】Grid-GCN for Fast and Scalable Point Cloud Learning (CVPR 2020)
- 8. 【點雲識別】Adaptive Hierarchical Down-Sampling for Point Cloud Classification(CVPR 2020)
- 9. 【點雲識別】AdaCoSeg: Adaptive Shape Co-Segmentation with Group Consistency Loss (CVPR 2020 oral)
- 10. [SGN]SGN: Sequential Grouping Networks for Instance Segmentation