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

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...

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Feb. 21, 2019

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Plug and-train Loss for Single View 3D Reconstruction

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https://imatge-upc.github.io/mrl/

Obtaining 3D geometry from images is a well studied
problem by the computer vision community. In the concrete case of a single image, a considerable amount of prior
knowledge is often required to obtain plausible reconstructions. Recently, deep neural networks in combination with
3D morphable models (3DMM) have been used in order to
address the lack of scene information, leading to more accurate results. Nevertheless, the losses employed during the
training process are usually a linear combination of terms
where the coefficients, also called hyperparameters, must
be carefully tuned for each dataset to obtain satisfactory results. In this work we propose a hyperparameters-free loss
that exploits the geometry of the problem for learning 3D
reconstruction from a single image. The proposed formulation is not dataset dependent, is robust against very large
camera poses and jointly optimizes the shape of the object
and the camera pose

Universitat Politècnica de Catalunya

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Plug and-train Loss for Single View 3D Reconstruction

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?

https://imatge-upc.github.io/mrl/ Obtaining 3D geometry from images is a well studied problem by the computer vision community. In the concrete case of a single image, a considerable amount of prior knowledge is often required to obtain plausible reconstructions. Recently, deep neural networks in combination with 3D morphable models (3DMM) have been used in order to address the lack of scene information, leading to more accurate results. Nevertheless, the losses employed during the training process are usually a linear combination of terms where the coefficients, also called hyperparameters, must be carefully tuned for each dataset to obtain satisfactory results. In this work we propose a hyperparameters-free loss that exploits the geometry of the problem for learning 3D reconstruction from a single image. The proposed formulation is not dataset dependent, is robust against very large camera poses and jointly optimizes the shape of the object and the camera pose

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