读 Self-Supervised 3D Mesh Reconstruction from Single Images
source link: https://jyzhu.top/2021/10/21/Self-Supervised-3D-Mesh-Reconstruction-from-Single-Images/
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作者：Tao Hu, Liwei Wang, Xiaogang Xu, Shu Liu, Jiaya Jia
发表： CVPR 2021
Github链接： https://github.com/dvlab-research/SMR
如果你去做这个任务，会怎么做？作者做的方法和你想的有什么差异？
- 现在的单视角3D重建方法，通过仅仅对2D图像进行标注，已经可以做到重建物体的形状和质地了。但是如果没有3D层面的监督，效果不是很理想。
- 通过插补和预测生成的3D标注应该是一致的；所有图像的地标（landmark）的特征应该是一致的
What：
- 提出一个自监督 3D Mesh 重建方法（Self-supervised Mesh Reconstruction, SMR）
- SMR只需要轮廓 mask 作为标注，就可以重建自然界的物体，比如鸟、牛、自行车
- 这个方法在多个数据集上，都提升了2D有监督和无监督的3D mesh重建效果
- 这个方法还可以迁移到其他图像分析任务上，比如 novel view generation, shape transfer,和 texture transfer
读前疑问：
- 自监督是怎么应用在这里的？想不通哦
- landmark 的用途是什么？
- 我猜这里是不是使用插补和预测的方法，根据2D标注生成了3D标注呢？
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