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

Segmentation of unseen industrial parts is essential for autonomousindustrial systems. However, industrial components are texture-less,reflective, and often found in cluttered and unstructured environments withheavy occlusion, which makes it more challenging to deal with unseen objects.To tackle this problem, we present a synthetic data generation pipeline thatrandomizes textures via domain randomization to focus on the shape information.In addition, we propose an RGB-D Fusion Mask R-CNN with a confidence mapestimator, which exploits reliable depth information in multiple featurelevels. We transferred the trained model to real-world scenarios and evaluatedits performance by making comparisons with baselines and ablation studies. Wedemonstrate that our methods, which use only synthetic data, could be effectivesolutions for unseen industrial components segmentation.

Segmenting Unseen Industrial Components in a Heavy Clutter Using RGB-D Fusion and Synthetic Data