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

Deep learning models are the state-of-the-art methods for semantic pointcloud segmentation, the success of which relies on the availability oflarge-scale annotated datasets. However, it can be extremely time-consuming andprohibitively expensive to compile such datasets. In this work, we propose anactive learning approach to maximize model performance given limited annotationbudgets. We investigate the appropriate sample granularity for active selectionunder realistic annotation cost measurement (clicks), and demonstrate thatsuper-point based selection allows for more efficient usage of the limitedbudget compared to point-level and instance-level selection. We further exploitlocal consistency constraints to boost the performance of the super-point basedapproach. We evaluate our methods on two benchmarking datasets (ShapeNet andS3DIS) and the results demonstrate that active learning is an effectivestrategy to address the high annotation costs in semantic point cloudsegmentation.

Label-Efficient Point Cloud Semantic Segmentation: An Active Learning Approach