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

Research in 3D semantic segmentation has been increasing performance metrics,like the IoU, by scaling model complexity and computational resources, leavingbehind researchers and practitioners that (1) cannot access the necessaryresources and (2) do need transparency on the model decision mechanisms. Inthis paper, we propose SCENE-Net, a low-resource white-box model for 3D pointcloud semantic segmentation. SCENE-Net identifies signature shapes on the pointcloud via group equivariant non-expansive operators (GENEOs), providingintrinsic geometric interpretability. Our training time on a laptop is 85~min,and our inference time is 20~ms. SCENE-Net has 11 trainable geometricalparameters and requires fewer data than black-box models. SCENE--Net offersrobustness to noisy labeling and data imbalance and has comparable IoU tostate-of-the-art methods. With this paper, we release a 40~000 Km labeleddataset of rural terrain point clouds and our code implementation.

Low-Resource White-Box Semantic Segmentation of Supporting Towers on 3D Point Clouds via Signature Shape Identification