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

3D shape reconstruction from a single image is a highly ill-posed problem.Modern deep learning based systems try to solve this problem by learning anend-to-end mapping from image to shape via a deep network. In this paper, weaim to solve this problem via an online optimization framework inspired bytraditional methods. Our framework employs a deep autoencoder to learn a set oflatent codes of 3D object shapes, which are fitted by a probabilistic shapeprior using Gaussian Mixture Model (GMM). At inference, the shape and pose arejointly optimized guided by both image cues and deep shape prior withoutrelying on an initialization from any trained deep nets. Surprisingly, ourmethod achieves comparable performance to state-of-the-art methods even withouttraining an end-to-end network, which shows a promising step in this direction.

Single-view Object Shape Reconstruction Using Deep Shape Prior and Silhouette