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Plug-and-Train Loss for
Mode-Based Single View 3D
Reconstruction
Eduard Ramon, Jordi Villar, Guillermo Ruiz, Thomas Batard, Xavier Giró
Index
1. Introduction
2. Multiview Reprojection Loss
3. Experiments
4. Conclusions
Introduction: Single View 3D Reconstruction
x = g( I )
q, t = c( I )
Introduction: Standard losses
Standard Loss
MRL: Multiview Reprojection Loss
What about q and t?
MRL: Multiview Reprojection Loss
MRL: Multiview Reprojection Loss
MRL: Multiview Reprojection Loss
Experiments: Standard vs MRL
Experiments: Standard vs MRL
Experiments: MICC Dataset
Experiments: Robustness against input diversity
● 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.
Thank you!
Questions?

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Plug and-train Loss for Single View 3D Reconstruction