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

This paper presents an end-to-end convolutional neural network (CNN) for2D-3D exemplar detection. We demonstrate that the ability to adapt the featuresof natural images to better align with those of CAD rendered views is criticalto the success of our technique. We show that the adaptation can be learned bycompositing rendered views of textured object models on natural images. Ourapproach can be naturally incorporated into a CNN detection pipeline andextends the accuracy and speed benefits from recent advances in deep learningto 2D-3D exemplar detection. We applied our method to two tasks: instancedetection, where we evaluated on the IKEA dataset, and object categorydetection, where we out-perform Aubry et al. for "chair" detection on a subsetof the Pascal VOC dataset.

Deep Exemplar 2D-3D Detection by Adapting from Real to Rendered Views