Exploring Spatial Context for 3D Semantic Segmentation of Point Clouds[ICCV2017]
論文連接 \qquad PointNet將輸入的點細分爲一個個塊（個人理解爲訓練模型時的一個個Batch）並且獨立的處理這些Block。在本文中，作者在PointNet的基礎上提出了兩種擴展模型，增大了模型在3D場景中的感受野，從而使模型可以處理更大尺度的空間場景。 \qquad 將點塊轉換爲multi-scale blocks和grid blocks，以獲得input-level的上下文。然後，
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相關文章
- 1. 3D Recurrent Neural Networks with Context Fusion for Point Cloud Semantic Segmentation[ECCV2018]
- 2. Context Encoding for Semantic Segmentation[CVPR2018]
- 3. Context Encoding for Semantic Segmentation
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- 5. Recurrent Slice Networks for 3D Segmentation of Point Clouds[CVPR2018]
- 6. 【原】Context Encoding for Semantic Segmentation
- 7. Hierarchical Point-Edge Interaction Network for Point Cloud Semantic Segmentation
- 8. Scan Context: Egocentric Spatial Descriptor for Place Recognition within 3D Point Cloud Map
- 9. [譯文] PASS3D: Precise and Accelerated Semantic Segmentation for 3D Point Cloud
- 10. 【譯文】 PASS3D: Precise and Accelerated Semantic Segmentation for 3D Point Cloud