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 method to learn the 3D surface of objects directly from acollection of images. Previous work achieved this capability by exploitingadditional manual annotation, such as object pose, 3D surface templates,temporal continuity of videos, manually selected landmarks, andforeground/background masks. In contrast, our method does not make use of anysuch annotation. Rather, it builds a generative model, a convolutional neuralnetwork, which, given a noise vector sample, outputs the 3D surface and textureof an object and a background image. These 3 components combined with anadditional random viewpoint vector are then fed to a differential renderer toproduce a view of the sampled object and background. Our general principle isthat if the output of the renderer, the generated image, is realistic, then itsinput, the generated 3D and texture, should also be realistic. To achieverealism, the generative model is trained adversarially against a discriminatorthat tries to distinguish between the output of the renderer and real imagesfrom the given data set. Moreover, our generative model can be paired with anencoder and trained as an autoencoder, to automatically extract the 3D shape,texture and pose of the object in an image. Our trained generative model andencoder show promising results both on real and synthetic data, whichdemonstrate for the first time that fully unsupervised 3D learning from imagecollections is possible.

Unsupervised 3D Shape Learning from Image Collections in the Wild