Modeling Local Geometric Structure of 3D Point Clouds using Geo-CNN 速記
Modeling Local Geometric Structure of 3D Point Clouds using Geo-CNN 廣告 歡迎加入圖網絡交流羣：832405795 背景 作者認爲直接以邊向量通過MLP獲得權重容易過擬合，因爲邊的幾何結構多變，方差大；另外，直接映射至高維空間有可能丟失原有的幾何結構，因此需要關注顯式幾何結構建模 改造局部點雲中鄰點與中心點的特徵提取方式：首先將邊
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相關文章
- 1. Modeling Local Geometric Structure of 3D Point Clouds using Geo-CNN
- 2. 【點雲識別】Modeling Local Geometric Structure of 3D Point Clouds using Geo-CNN (CVPR 2019)
- 3. 3D Point Clouds-1
- 4. Recurrent Slice Networks for 3D Segmentation of Point Clouds[CVPR2018]
- 5. Literature Review: Incremental Segment-Based Localization in 3D Point Clouds
- 6. Point Cloud 2019
- 7. Paper5：Curved-Voxel Clustering for Accurate Segmentation of 3D LiDAR Point Clouds with Real-Time Per
- 8. Vote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient Convolutional Neural Networks
- 9. Vote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient CNN論文綜述（2016）
- 10. Unsupervised Learning of Depth and Ego-Motion from Monocular Video Using 3D Geometric Constraints