USIP: Unsupervised Stable Interest Point Detection from 3D Point Clouds 2019 论文笔记
新加坡国立大学 代码链接:https://github.com/lijx10/USIP 这是一篇通过无监督学习来进行点云关键点检测的文章. 整体的网络结构: 首先将输入的点云进行随机旋转平移变换，网络分别对两个点云进行关键点提取得到关键点 Q Q Q，并对每个关键点的不确定度进行预测得到 ∑ \sum ∑。 损失函数 Probabilistic Chamfer Loss 作者希望网络能够学习点云中
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相关文章
- 1. [ICCV 2019] USIP: Unsupervised Stable Interest Point Detection from 3D Point Clouds
- 2. Deep Hough Voting for 3D Object Detection in Point Clouds 论文笔记
- 3. 3D Point Clouds-1
- 4. PointPillars: Fast Encoders for 3D Object Detection from Point Clouds
- 5. PIXOR Real-time 3D Object Detection from Point Clouds
- 6. Point Cloud 2019
- 7. Unsupervised Learning of Shape and Pose with Differentiable Point Clouds && NIPS 2018 论文笔记
- 8. 【论文阅读】SSN: Shape Signature Networks for Multi-class Object Detection from Point Clouds
- 9. 《SuperPoint: Self-Supervised Interest Point Detection and Description》论文笔记
- 10. SuperPoint:Self-Supervised Interest Point Detection and Description 论文笔记