DSAC - Differentiable RANSAC for Camera Localization
可以应用到任意的deeplearning的localization的模型中 R(.）表示的是refine 为了使整个过程可微：，这样使用每个w时候的最小集合的采样的概率来区分是好的模型还是不好的模型 然后将，这样就将ransac中的硬决策转换成了软决策 几个ransac的对比： 上述中，表示的是在w和v的情况下选择匹配集合J，然后计算出模型的得分，因此将会inl
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相关文章
- 1. DSAC – Differentiable RANSAC for Camera Localization && CVPR 2017 论文笔记
- 2. 【论文笔记】PoseGAN: A Pose-to-Image Translation Framework for Camera Localization
- 3. Full-Frame Scene Coordinate Regression for Image-Based Localization
- 4. Learning Less is More – 6D Camera Localization via 3D Surface Regression && 2018 论文笔记
- 5. Learning Less is More – 6D Camera Localization via 3D Surface Regression
- 6. CamNet: Coarse-to-Fine Retrieval for Camera Re-Localization 论文笔记
- 7. Camera Relocalization by Exploiting Multi-View Constraints for Scene Coordinates Regression 2019论文笔记
- 8. 【论文笔记】Privacy Preserving Image Queries for Camera Localization
- 9. ViPR: Visual-Odometry-aided Pose Regression for 6DoF Camera Localization CVPR 2020
- 10. Hierarchical Scene Coordinate Classification and Regressionfor Visual Localization 2020 论文笔记 视觉重定位