1.
Plug-and-Train Loss for
Mode-Based Single View 3D
Reconstruction
Eduard Ramon, Jordi Villar, Guillermo Ruiz, Thomas Batard, Xavier Giró

2.
Index
1. Introduction
2. Multiview Reprojection Loss
3. Experiments
4. Conclusions

3.
Introduction: Single View 3D Reconstruction
x = g( I )
q, t = c( I )

4.
Introduction: Standard losses
Standard Loss

5.
MRL: Multiview Reprojection Loss
What about q and t?

6.
MRL: Multiview Reprojection Loss

7.
MRL: Multiview Reprojection Loss

8.
MRL: Multiview Reprojection Loss

9.
Experiments: Standard vs MRL

10.
Experiments: Standard vs MRL

11.
Experiments: MICC Dataset

12.
Experiments: Robustness against input diversity

13.
● The MRL can train single view reconstruction models without adding extra parameters.
Conclusions
● Robust models against large poses and diversity of faces.
● Effective at jointly optimizing geometry and camera pose.
● Comparable metrics against standard losses with a single train.

14.
Thank you!
Questions?