Modeling Local Geometric Structure of 3D Point Clouds using Geo-CNN 速记
Modeling Local Geometric Structure of 3D Point Clouds using Geo-CNN 广告 欢迎加入图网络交流群：832405795 背景 作者认为直接以边向量通过MLP获得权重容易过拟合，因为边的几何结构多变，方差大；另外，直接映射至高维空间有可能丢失原有的几何结构，因此需要关注显式几何结构建模 改造局部点云中邻点与中心点的特征提取方式：首先将边
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相关文章
- 1. Modeling Local Geometric Structure of 3D Point Clouds using Geo-CNN
- 2. 【点云识别】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论文综述（2016）
- 10. Unsupervised Learning of Depth and Ego-Motion from Monocular Video Using 3D Geometric Constraints