[譯文] PASS3D: Precise and Accelerated Semantic Segmentation for 3D Point Cloud
PASS3D：精確、快速的三維點語義分割點雲 摘要 在這篇文章中，我們提出了PASS3D去實現逐點級的三維點雲語義分割。我們的框架結合了傳統幾何方法的高效和深度學習方法的魯棒性，包含兩個階段：階段1，加速聚類生成算法會通過分割無地面的點雲生成優化的聚類提案，能夠在極短時間內生成較少冗餘和較高召回率；階段2，通過神經網絡對這些聚類提案進行放大和進一步處理，估計每個點的語義，同時提出一種新的數據增
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相關文章
- 1. 【譯文】 PASS3D: Precise and Accelerated Semantic Segmentation for 3D Point Cloud
- 2. Hierarchical Point-Edge Interaction Network for Point Cloud Semantic Segmentation
- 3. 3D Recurrent Neural Networks with Context Fusion for Point Cloud Semantic Segmentation[ECCV2018]
- 4. 《PointNet：Deep Learning on Point Sets for 3D Classification and Segmentation》中文翻譯
- 5. 【2019CVPR學習】翻譯-Graph Attention Convolution for Point Cloud Semantic Segmentation
- 6. 論文筆記-PointSeg: Real-Time Semantic Segmentation Based on 3D LiDAR Point Cloud
- 7. PointNet:Deep Learning on Point Sets for 3D Classificatiion and Segmentation
- 8. [Point Cloud] PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation 論文解讀
- 9. Deep Learning on Point Sets for 3D Classification and Segmentation
- 10. 【論文筆記】【點雲分割】2018_PointSIFT：A SIFT-like Network Module for 3D Point Cloud Semantic Segmentation