PatchNets: Patch-Based Generalizable Deep Implicit 3D Shape Representations
<PatchNets>
PatchNets: Patch-based generalizable deep implicit 3D shape representations目录
Motivation
- mid-level patch-based SDF
- 因为在patch层次,不同类别的物体有相似性,用上这种相似性就可以做更泛化的模型
- 在一个canonical space下学到这些patch-based representation
- 从ShapeNet的一个类别学出来的representation,可以用于表征任何一个其他类别的非常细节的shapes;并且可以用更少的shape来训练
Overview
- auto-decoder
- losses:重建loss和patch extrinsics的guidance loss,还有regularization
extrinsic loss
- 这个loss保证所有的patch都对surface有贡献,并且处于caonical space
- 第i个物体的patch extrinsics: $\boldsymbol{e}i=[\boldsymbol{e}{i,0},\boldsymbol{e}{i,1},\ldots,\boldsymbol{e}{i,N_P-1}]$
- $ \mathcal{L}_{ext}(\boldsymbol{e}i) = \mathcal{L}{sur}(\boldsymbol{e}i) + \mathcal{L}{cov}(\boldsymbol{e}i) + \mathcal{L}{rot}(\boldsymbol{e}i) + \mathcal{L}{scl}(\boldsymbol{e}i) + \mathcal{L}{var}(\boldsymbol{e}_i) $
- $\mathcal{L}_{sur}(\boldsymbol{e}_i)$ 保证每个patch都离surface很近
- $\underset{逐patch}{\max}[surface上的所有点到该patch距离的最小值]$
- $\mathcal{L}_{cov}(\boldsymbol{e}_i)$ symmetric coverage loss,鼓励surface上的每个点都至少被一个patch涵盖
- $\mathcal{L}_{rot}(\boldsymbol{e}_i)$ 把patches和surface normals对齐
- $\mathcal{L}_{scl}(\boldsymbol{e}_i)$ 鼓励patches to be reasonably small,防止不同patch之间显著的重叠
- $\mathcal{L}_{var}(\boldsymbol{e}_i)$ 鼓励所有patch大小相似