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fin de séance 2

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Co-authored-by: Damien Guillotin <damguillotin@gmail.com>
Co-authored-by: pejour <pejour@users.noreply.github.com>
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# Usefull resources # BE PI3D : MVS par level set
## Level set method, Wikipedia ## Usefull resources
### Level set method, Wikipedia
https://en.wikipedia.org/wiki/Level-set_method https://en.wikipedia.org/wiki/Level-set_method
![](https://upload.wikimedia.org/wikipedia/commons/7/78/Level_set_method.png) ![](https://upload.wikimedia.org/wikipedia/commons/7/78/Level_set_method.png)
## Level set, Wikipedia ### Level set, Wikipedia
https://en.wikipedia.org/wiki/Level_set https://en.wikipedia.org/wiki/Level_set
## TP3, Stéréoscopie multi-vues (MVS) ### TP3, Stéréoscopie multi-vues (MVS)
https://moodle-n7.inp-toulouse.fr/pluginfile.php/116746/mod_resource/content/2/sujet_TP3.pdf https://moodle-n7.inp-toulouse.fr/pluginfile.php/116746/mod_resource/content/2/sujet_TP3.pdf
## Coding Adventure: Terraforming, Sebastian Lague ### Coding Adventure: Terraforming, Sebastian Lague
[![](https://i.ytimg.com/vi_webp/vTMEdHcKgM4/maxresdefault.webp)](https://www.youtube.com/watch?v=vTMEdHcKgM4) [![](https://i.ytimg.com/vi_webp/vTMEdHcKgM4/maxresdefault.webp)](https://www.youtube.com/watch?v=vTMEdHcKgM4)
## 3D Signed Distance function (NVIDIA Research) ### 3D Signed Distance function (NVIDIA Research)
2022 2022
Towaki Takikawa Towaki Takikawa
@ -27,7 +29,7 @@ Morgan McGuire
https://tovacinni.github.io/sdf-explorer/ https://tovacinni.github.io/sdf-explorer/
## A GPU Implementation of Level Set Multiview Stereo ### A GPU Implementation of Level Set Multiview Stereo
2006 2006
Patrick Labatut Patrick Labatut
@ -37,7 +39,7 @@ Jean-Philippe Pons
https://link.springer.com/chapter/10.1007/11758549_33 https://link.springer.com/chapter/10.1007/11758549_33
https://link.springer.com/content/pdf/10.1007/11758549_33.pdf?pdf=inline%20link https://link.springer.com/content/pdf/10.1007/11758549_33.pdf?pdf=inline%20link
## Fast Level Set Multi-View Stereo on Graphics Hardware ### Fast Level Set Multi-View Stereo on Graphics Hardware
2006 2006
Patrick Labatut Patrick Labatut
@ -47,7 +49,7 @@ Jean-Philippe Pons
https://ieeexplore.ieee.org/document/4155801 https://ieeexplore.ieee.org/document/4155801
https://sci-hub.hkvisa.net/10.1109/3dpvt.2006.62 https://sci-hub.hkvisa.net/10.1109/3dpvt.2006.62
## Model-based multiview stereo via level sets with statistical shape prior ### Model-based multiview stereo via level sets with statistical shape prior
2011 2011
Moumen El-Melegy Moumen El-Melegy
@ -57,20 +59,22 @@ Aly A. Farag
https://ieeexplore.ieee.org/document/6365368 https://ieeexplore.ieee.org/document/6365368
https://sci-hub.hkvisa.net/https://ieeexplore.ieee.org/document/6365368 https://sci-hub.hkvisa.net/https://ieeexplore.ieee.org/document/6365368
## Équation bizarre de l'idée générale ### Équation bizarre de l'idée générale
`g(x,y)=exp(-((0.2*x)^(2)+((y^(2))/(x^(2)+0.1))))*sig(-10*x)-5*exp(-((y^(2))/((0.1*x)^(2))))*sig(10*x)` `g(x,y)=exp(-((0.2*x)^(2)+((y^(2))/(x^(2)+0.1))))*sig(-10*x)-5*exp(-((y^(2))/((0.1*x)^(2))))*sig(10*x)`
$$g(x,y) = \exp\left(-\left(\left(0.2x\right)^2 + \left(\frac{y^2}{x^2 + 0.1}\right)\right)\right)\sigma\left(-10x\right) - 5\exp\left(-\frac{y^2}{\left(0.1x\right)^2}\right)\sigma\left(10x\right)$$ $$g(x,y) = \exp\left(-\left(\left(0.2x\right)^2 + \left(\frac{y^2}{x^2 + 0.1}\right)\right)\right)\sigma\left(-10x\right) - 5\exp\left(-\frac{y^2}{\left(0.1x\right)^2}\right)\sigma\left(10x\right)$$
## A Level-Set Approach to 3D Reconstruction from Range Data ### A Level-Set Approach to 3D Reconstruction from Range Data
1998 1998
Ross T. Whitaker Ross T. Whitaker
https://www.cs.utah.edu/~whitaker/sceneRecon/papers/sparse_report.pdf
https://link.springer.com/article/10.1023/A:1008036829907 https://link.springer.com/article/10.1023/A:1008036829907
https://link.springer.com/content/pdf/10.1023/A:1008036829907.pdf?pdf=button
## Level Set Methods in Computer Vision (Slides) ### Level Set Methods in Computer Vision (Slides)
2006 2006
Daniel Cremers Daniel Cremers
@ -81,7 +85,7 @@ https://www.csd.uwo.ca/~yboykov/Presentations/ECCV06_tutorial_partII_dan.pdf
On part d'un solide qu'on rogne en soustrayant les valeurs obtenues avec les gaussiennes => jusqu'à obtenir le solide étudié. On part d'un solide qu'on rogne en soustrayant les valeurs obtenues avec les gaussiennes => jusqu'à obtenir le solide étudié.
## Variational principles, surface evolution, PDEs, level set methods, and the stereo problem ### Variational principles, surface evolution, PDEs, level set methods, and the stereo problem
1996/1998 1996/1998
Olivier Faugeras Olivier Faugeras
@ -90,7 +94,7 @@ Renaud Keriven
https://hal.inria.fr/inria-00073673/document https://hal.inria.fr/inria-00073673/document
https://ieeexplore.ieee.org/document/661183 https://ieeexplore.ieee.org/document/661183
## Multi-View Stereo: A Tutorial ### Multi-View Stereo: A Tutorial
2015 2015
Yasutaka Furukawa Yasutaka Furukawa
@ -98,9 +102,48 @@ Carlos Hernández
https://carlos-hernandez.org/papers/fnt_mvs_2015.pdf https://carlos-hernandez.org/papers/fnt_mvs_2015.pdf
### amy-tabb/tabb-level-set-segmentation
## JDD https://github.com/amy-tabb/tabb-level-set-segmentation
### Ramesh-X/Level-Set
https://github.com/Ramesh-X/Level-Set
### 3D Image Reconstruction and Level Set Methods
Introduction and derivation of the level set methods.
https://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=3811&context=etd
# Les bons tuyaux de Lilian
 
Voici les références pour le groupe de BE de Laurant, des références pour lesquelles la reconstruction 3D sont les zeros d'un level set (entre parenthèses : la nature de la fonction dont les paramètres sont optimisés) :
- IDR (fonction de distance signée) : Multiview Neural Surface Reconstruction by Disentangling Geometry and Appearance
- Neus (fonction de distance signée) : NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstruction
- Unisurf (fonction d'occupation, terme traduit de l'anglais occupancy) : UNISURF, Unifying Neural Implicit Surfaces and Radiance Fields for Multi-View Reconstruction
- VolSDF (fonction de distance signée) : Volume rendering of neural implicit surfaces
 
Les quatre méthodes font du rendu dit surfacique, les trois dernières (Neus, Unisurf and VolSDF) étant "hybrides", à savoir utilisant du rendu surfacique et volumique.
 
N'hésitez pas à nous demander plus d'informations si besoin.
 
Une autre référence avec un mélange de MVS et de PS qui considère le niveau d'incertitude des deux méthodes respectivement pour utiliser en chaque pixel le résultat le plus fiable (level set et equation eikonale dans leur fonction de cout) :
https://openaccess.thecvf.com/content/CVPR2022/html/Kaya_Uncertainty-Aware_Deep_Multi-View_Photometric_Stereo_CVPR_2022_paper.html
Un autre article à lire :
https://hal.inria.fr/inria-00266287/file/GargalloPradosSturm-iccv2007.pdf
 
# Note
Eviter papier de BOYKOV CREMERS Eviter papier de BOYKOV CREMERS
# A demander
Quel estla différence avec la méthode vu en TP ?
- On calcule le MVS comme d'hab
-> Carte de profondeur par image
- Pas de projection en nuage de points dense
-> Utilisation de la carte de prof pour modifier le level set initiale ?