**ManifoldPlus** is a method for robust and scalable conversion of triangle soups to watertight manifolds. It extracts exterior faces between occupied voxels and empty voxels, and uses a projection based optimization method to accurately recover a watertight manifold that resembles the reference mesh. It does not rely on face normals of the input triangle soups and can accurately recover zero-volume structures. For scalability, it employs an adaptive Gauss-Seidel method for shape optimization, in which each step is an easy-to-solve convex problem.

**Wasserstein GAN**, or **WGAN**, is a type of generative adversarial network that minimizes an approximation of the Earth-Mover's distance (EM) rather than the Jensen-Shannon divergence as in the original [GAN](https://paperswithcode.com/method/gan) formulation. It leads to more stable training than original GANs with less evidence of mode collapse, as well as meaningful curves that can be used for debugging and searching hyperparameters.

WGAN

Wasserstein GAN

ManifoldPlus

ManifoldPlus: A Robust and Scalable Watertight Manifold Surface Generation Method for Triangle Soups

**Contextual Residual Aggregation**, or **CRA**, is a module for image inpainting. It can produce high-frequency residuals for missing contents by weighted aggregating residuals from contextual patches, thus only requiring a low-resolution prediction from the network. Specifically, it involves a neural network to predict a low-resolution inpainted result and up-sample it to yield a large blurry image. Then we produce the high-frequency residuals for in-hole patches by aggregating weighted high-frequency residuals from contextual patches. Finally, we add the aggregated residuals to the large blurry image to obtain a sharp result.

Source	ManifoldPlus: A Robust and Scalable Watertight Manifold Surface Generation Method for Triangle Soups
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com