2015-CVPR-Direction Matters_ Depth Estimation with a Surface Normal Classifier
2015-CVPR-Direction Matters: Depth Estimation with a Surface Normal Classifier abstract 用分类器对整个集合法向量进行分类，通过一系列优化最终决定surface orientation(表面方向） introduciton 用双目矫正图片对学习视差的局限性： 条纹少的地方，如墙 过度曝光的地方 输入数据本身就很模
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相关文章
- 1. 00040-Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-Scale Convolutional
- 2. 每天一篇论文 306/365Self-supervised Learning for Single View Depth and Surface Normal Estimation
- 3. 论文《Depth Estimation From a Light Field Image Pair With a Generative Model》学习
- 4. Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-Scale... 论文笔记
- 5. Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-Scale Convolutional Archit
- 6. Predicting Depth,Surface Normals and Semantic Labels with a Common Multi-Scale Convolutional Archite
- 7. 11.Unsupervised Monocular Depth Estimation with Left-Right Consistency
- 8. TRAINING A CLASSIFIER
- 9. Training a classifier
- 10. From Depth Data to Head Pose Estimation: a Siamese approach----论文阅读笔记