《論文閱讀》Grid-GCN for Fast and Scalable Point Cloud Learning
留個筆記自用 Grid-GCN for Fast and Scalable Point Cloud Learning 做什麼 首先先得理解點雲是什麼 點雲的概念：點雲是在同一空間參考系下表達目標空間分佈和目標表面特性的海量點集合，在獲取物體表面每個採樣點的空間座標後，得到的是點的集合，稱之爲「點雲」（Point Cloud）。 點包含了豐富的信息，包括三維座標X，Y，Z、顏色、分類值、強度值、時間
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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. 【點雲系列】Grid-GCN for Fast and Scalable Point Cloud Learning
- 3. 論文閱讀：PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
- 4. 論文閱讀：EfficientDet: Scalable and Efficient Object Detection
- 5. 閱讀論文Learning Fast and Robust Target Models for Video Object Segmentation
- 6. 論文：Scalable and accurate deep learning for electronic health records研讀筆記
- 7. [Point Cloud] PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation 論文解讀
- 8. node2vec: Scalable Feature Learning for Networks論文解讀
- 9. [論文閱讀筆記] node2vec Scalable Feature Learning for Networks
- 10. 【論文閱讀】【綜述】Deep Learning for 3D Point Clouds: A Survey