English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The reconstruction of a discrete surface from a point cloud is a fundamentalgeometry processing problem that has been studied for decades, with manymethods developed. We propose the use of a deep neural network as a geometricprior for surface reconstruction. Specifically, we overfit a neural networkrepresenting a local chart parameterization to part of an input point cloudusing the Wasserstein distance as a measure of approximation. By jointlyfitting many such networks to overlapping parts of the point cloud, whileenforcing a consistency condition, we compute a manifold atlas. By samplingthis atlas, we can produce a dense reconstruction of the surface approximatingthe input cloud. The entire procedure does not require any training data orexplicit regularization, yet, we show that it is able to perform remarkablywell: not introducing typical overfitting artifacts, and approximating sharpfeatures closely at the same time. We experimentally show that this geometricprior produces good results for both man-made objects containing sharp featuresand smoother organic objects, as well as noisy inputs. We compare our methodwith a number of well-known reconstruction methods on a standard surfacereconstruction benchmark.

Deep Geometric Prior for Surface Reconstruction