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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Shape registration is the process of aligning one 3D model to another. Mostprevious methods to align shapes with no known correspondences attempt to solvefor both the transformation and correspondences iteratively. We present a shaperegistration approach that solves for the transformation using fuzzycorrespondences to maximize the overlap between the given shape and the targetshape. A coarse to fine approach with Levenberg-Marquardt method is used foroptimization. Real and synthetic experiments show our approach is robust andoutperforms other state of the art methods when point clouds are noisy, sparse,and have non-uniform density. Experiments show our method is more robust toinitialization and can handle larger scale changes and rotation than othermethods. We also show that the approach can be used for 2D-3D alignment viaray-point alignment.

Robust Shape Registration using Fuzzy Correspondences