論文閱讀： Rotation Invariant Convolutions for 3D Point Clouds Deep Learning
論文題目： Rotation Invariant Convolutions for 3D Point Clouds Deep Learning. 3DV 2019 【tensorflow】 論文特點： 解決點雲旋轉問題 方法： 點雲受旋轉影響主要有兩個：輸入特徵和提取局部特徵的過程 1）特徵改爲旋轉不變的特徵：兩條邊長，兩個角度 2）旋轉不變的卷積，類似shellnet，按中心點和重心連線方向劃分
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相關文章
- 1. 【論文閱讀】【綜述】Deep Learning for 3D Point Clouds: A Survey
- 2. Deep Learning for 3D Point Clouds: A Survey
- 3. 【論文閱讀】-2019-CLASSIFICATION OF AERIAL POINT CLOUDS WITH DEEP LEARNING
- 4. 論文閱讀：PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
- 5. 3D Point Clouds-1
- 6. 論文閱讀：Permutation Matters: Anisotropic Convolutional Layer for Learning on Point Clouds
- 7. 3D點雲深度學習」綜述論文--Deep Learning for 3D Point Clouds: A Survey
- 8. 論文閱讀： Rotation Invariant Point Cloud Classification: Where Local Geometry Meets Global Topology
- 9. Deep Hough Voting for 3D Object Detection in Point Clouds 論文筆記
- 10. 【圖像去模糊】《Blur-Invariant Deep Learning for Blind-Deblurring》論文閱讀