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

As the development of 3D sensors, registration of 3D data (e.g. point cloud)coming from different kind of sensor is dispensable and shows great demanding.However, point cloud registration between different sensors is challengingbecause of the variant of density, missing data, different viewpoint, noise andoutliers, and geometric transformation. In this paper, we propose a method tolearn a 3D descriptor for finding the correspondent relations between thesechallenging point clouds. To train the deep learning framework, we usesynthetic 3D point cloud as input. Starting from synthetic dataset, we useregion-based sampling method to select reasonable, large and diverse trainingsamples from synthetic samples. Then, we use data augmentation to extend ournetwork be robust to rotation transformation. We focus our work on more generalcases that point clouds coming from different sensors, named cross-source pointcloud. The experiments show that our descriptor is not only able to generalizeto new scenes, but also generalize to different sensors. The resultsdemonstrate that the proposed method successfully aligns two 3D cross-sourcepoint clouds which outperforms state-of-the-art method.

Learning a 3D descriptor for cross-source point cloud registration from synthetic data