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![1
DEEP LEARNING JP
[DL Papers]
http://deeplearning.jp/
MeshとDeep Learning
Surface Networks & AtlasNet
Presenter: Joji Toya...](https://image.slidesharecdn.com/dlhacks08101-180822143108/85/DL-Mesh-Deep-Learning-Surface-Networks-AtlasNet-1-320.jpg?cb=1534948383)
![10
手法概要
• 従来手法では，潜在表現から3D点をそのまま出力．
– これだけでは，点群しか再構成されない
• 本手法では，潜在表現と正方形]0,1[2
からサンプリングされた点を入力として，3D
点を出力
– こうすることで，元の正方形は...](https://image.slidesharecdn.com/dlhacks08101-180822143108/85/DL-Mesh-Deep-Learning-Surface-Networks-AtlasNet-10-320.jpg?cb=1534948383)
![12
正方形から多様体への写像
• 正方形から２次元多様体（＝サーフェス）へのparameterizationをする関
数𝜓 𝜃考える．
– min 𝐿( 𝜓 𝜃 ]0,1[2
, 𝑆)を最小化したい
• 関数𝜓 𝜃は以下を満たしてほしい
– ...](https://image.slidesharecdn.com/dlhacks08101-180822143108/85/DL-Mesh-Deep-Learning-Surface-Networks-AtlasNet-12-320.jpg?cb=1534948383)
![15
再掲：手法概要
• 従来手法では，潜在表現から3D点をそのまま出力．
– これだけでは，点群しか再構成されない
• 本手法では，潜在表現と正方形]0,1[2
からサンプリングされた点を入力として，3D
点を出力
– こうすることで，元の正...](https://image.slidesharecdn.com/dlhacks08101-180822143108/85/DL-Mesh-Deep-Learning-Surface-Networks-AtlasNet-15-320.jpg?cb=1534948383)
