论文标题
为生成3D建模耦合显式和隐式表面表示
Coupling Explicit and Implicit Surface Representations for Generative 3D Modeling
论文作者
论文摘要
我们提出了一种新的神经结构，用于代表3D表面，它利用了两个互补的形状表示：（i）通过地图集的明确表示，即2D域的嵌入到3D中； （ii）隐式功能表示形式，即在3D体积上的标量函数，其级别表示表面。我们通过引入新的一致性损失来确保这两种表示，以确保从Atlas产生的表面与隐式函数的级别保持一致。我们的混合体系结构输出结果优于两个等效单代占领网络的输出，从而产生具有更准确的正态的更光滑的显式表面，并且具有更准确的隐式占用函数。此外，我们的表面重建步骤可以直接利用基于ATLA的显式表示。该过程在计算上是有效的，可以通过可区分的栅格式来直接使用，从而使我们的混合表示具有基于图像的损失。
We propose a novel neural architecture for representing 3D surfaces, which harnesses two complementary shape representations: (i) an explicit representation via an atlas, i.e., embeddings of 2D domains into 3D; (ii) an implicit-function representation, i.e., a scalar function over the 3D volume, with its levels denoting surfaces. We make these two representations synergistic by introducing novel consistency losses that ensure that the surface created from the atlas aligns with the level-set of the implicit function. Our hybrid architecture outputs results which are superior to the output of the two equivalent single-representation networks, yielding smoother explicit surfaces with more accurate normals, and a more accurate implicit occupancy function. Additionally, our surface reconstruction step can directly leverage the explicit atlas-based representation. This process is computationally efficient, and can be directly used by differentiable rasterizers, enabling training our hybrid representation with image-based losses.
