论文研读：2017 SfM Net Learning of Structure and Motion from Video
简介：这篇文章的创新之处在于打破传统的无监督深度估计时需要用双目图片进行自监督，sfm-net只需要单目的视频流就能够恢复深度图、点云图、相机姿态估计、场景运动刚体姿态估计、场景运动刚体mask。总之是一个非常强大的网络。 本文主要对sfm-ne论文的摘要以及网络结构进行解读，实现细节与实验分析有兴趣的可以下全文去读一下。 一.摘要 1.本文提出了SFM-NET：感知几何神经网络；按照场景与物体深
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- 1. SfM: Structure from motion
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相关文章
- 1. SfM: Structure from motion
- 2. CVPR 2017【论文笔记】Unsupervised Learning of Depth and Ego-Motion from Video
- 3. Unsupervised Learning of Depth and Ego-Motion from Video论文见解
- 4. 《Unsupervised Learning of Depth and Ego-Motion from Video》论文笔记
- 5. 从内外参到 Structure From Motion(SFM)
- 6. Structure from Motion 资料总结
- 7. Structure-from-Motion Revisited
- 8. Structure From Motion(SFM)入门讲解
- 9. 论文研读：2016 DeMoN Depth and Motion Network for Learning Monocular Stereo
- 10. Unsupervised Learning of Depth and Ego-Motion from Monocular Video Using 3D Geometric Constraints