PF-Net: Point Fractal Network for 3D Point Cloud Completion 2020 论文笔记
上海交通大学，商汤科技 自己的想法：拿这个作为数据预处理模块挺好的，不知道速度怎么样。GAN的损失函数不太了解，导致其中GAN鉴别器的应用部分有点没看懂，一般设计网络的时候可能到计算Chamber loss就可以了，但是这样生成的点云不够自然，所以加入了GAN让点云看起来更像真的，试验结果也显示GAN对性能提升有一定作用。 论文简介 从激光雷达等设备中获取的点云往往有所缺失，这给点云的后续处理带来
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相关文章
- 1. 03 PF-Net: Point Fractal Network for 3D Point Cloud Completion
- 2. 论文笔记 - Cascaded Refinement Network for Point Cloud Completion - 2020 CVPR
- 3. 3DRegNet: A Deep Neural Network for 3D Point Registration 2020论文笔记
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- 9. 【论文笔记】【点云分割】2018_PointSIFT：A SIFT-like Network Module for 3D Point Cloud Semantic Segmentation
- 10. DeepICP: An End-to-End Deep Neural Network for 3D Point Cloud Registration 2019 论文笔记