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

In this paper, we present a new method for the multiview registration ofpoint cloud. Previous multiview registration methods rely on exhaustivepairwise registration to construct a densely-connected pose graph and applyIteratively Reweighted Least Square (IRLS) on the pose graph to compute thescan poses. However, constructing a densely-connected graph is time-consumingand contains lots of outlier edges, which makes the subsequent IRLS struggle tofind correct poses. To address the above problems, we first propose to use aneural network to estimate the overlap between scan pairs, which enables us toconstruct a sparse but reliable pose graph. Then, we design a novel historyreweighting function in the IRLS scheme, which has strong robustness to outlieredges on the graph. In comparison with existing multiview registration methods,our method achieves 11% higher registration recall on the 3DMatch dataset and~13% lower registration errors on the ScanNet dataset while reducing ~70%required pairwise registrations. Comprehensive ablation studies are conductedto demonstrate the effectiveness of our designs.

Robust Multiview Point Cloud Registration with Reliable Pose Graph Initialization and History Reweighting