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/

Zendy Plus

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

Zendy Tools

Zendy Open

We present a novel 3D shape reconstruction method which learns to predict animplicit 3D shape representation from a single RGB image. Our approach uses aset of single-view images of multiple object categories without viewpointannotation, forcing the model to learn across multiple object categorieswithout 3D supervision. To facilitate learning with such minimal supervision,we use category labels to guide shape learning with a novel categorical metriclearning approach. We also utilize adversarial and viewpoint regularizationtechniques to further disentangle the effects of viewpoint and shape. We obtainthe first results for large-scale (more than 50 categories) single-viewpointshape prediction using a single model without any 3D cues. We are also thefirst to examine and quantify the benefit of class information in single-viewsupervised 3D shape reconstruction. Our method achieves superior performanceover state-of-the-art methods on ShapeNet-13, ShapeNet-55 and Pascal3D+.

Planes vs. Chairs: Category-guided 3D shape learning without any 3D cues