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

Estimating 6D object poses is a major challenge in 3D computer vision.Building on successful instance-level approaches, research is shifting towardscategory-level pose estimation for practical applications. Currentcategory-level datasets, however, fall short in annotation quality and posevariety. Addressing this, we introduce HouseCat6D, a new category-level 6D posedataset. It features 1) multi-modality with Polarimetric RGB and Depth(RGBD+P), 2) encompasses 194 diverse objects across 10 household categories,including two photometrically challenging ones, and 3) provides high-qualitypose annotations with an error range of only 1.35 mm to 1.74 mm. The datasetalso includes 4) 41 large-scale scenes with comprehensive viewpoint andocclusion coverage, 5) a checkerboard-free environment, and 6) dense 6Dparallel-jaw robotic grasp annotations. Additionally, we present benchmarkresults for leading category-level pose estimation networks.

HouseCat6D -- A Large-Scale Multi-Modal Category Level 6D Object Perception Dataset with Household Objects in Realistic Scenarios