《论文阅读》Grid-GCN for Fast and Scalable Point Cloud Learning
留个笔记自用 Grid-GCN for Fast and Scalable Point Cloud Learning 做什么 首先先得理解点云是什么 点云的概念：点云是在同一空间参考系下表达目标空间分布和目标表面特性的海量点集合，在获取物体表面每个采样点的空间坐标后，得到的是点的集合，称之为“点云”（Point Cloud）。 点包含了丰富的信息，包括三维坐标X，Y，Z、颜色、分类值、强度值、时间
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- 1. 【点云识别】Grid-GCN for Fast and Scalable Point Cloud Learning (CVPR 2020)
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相关文章
- 1. 【点云识别】Grid-GCN for Fast and Scalable Point Cloud Learning (CVPR 2020)
- 2. 【点云系列】Grid-GCN for Fast and Scalable Point Cloud Learning
- 3. 论文阅读：PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
- 4. 论文阅读：EfficientDet: Scalable and Efficient Object Detection
- 5. 阅读论文Learning Fast and Robust Target Models for Video Object Segmentation
- 6. 论文：Scalable and accurate deep learning for electronic health records研读笔记
- 7. [Point Cloud] PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation 论文解读
- 8. node2vec: Scalable Feature Learning for Networks论文解读
- 9. [论文阅读笔记] node2vec Scalable Feature Learning for Networks
- 10. 【论文阅读】【综述】Deep Learning for 3D Point Clouds: A Survey