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

Using generative models for Inverse Graphics is an active area of research.However, most works focus on developing models for supervised andsemi-supervised methods. In this paper, we study the problem of unsupervisedlearning of 3D geometry from single images. Our approach is to use a generativemodel that produces 2-D images as projections of a latent 3D voxel grid, whichwe train either as a variational auto-encoder or using adversarial methods. Ourcontributions are as follows: First, we show how to recover 3D shape and posefrom general datasets such as MNIST, and MNIST Fashion in good quality. Second,we compare the shapes learned using adversarial and variational methods.Adversarial approach gives denser 3D shapes. Third, we explore the idea ofmodelling the pose of an object as uniform distribution to recover 3D shapefrom a single image. Our experiment with the CelebA dataset\cite{liu2015faceattributes} proves that we can recover complete 3D shape froma single image when the object is symmetric along one, or more axis whilstresults obtained using ModelNet40 \cite{wu20153d} show the potentialside-effects, in which the model learns 3D shapes such that it can render thesame image from any viewpoint. Forth, we present a general end-to-end approachto learning 3D shapes from single images in a completely unsupervised fashionby modelling the factors of variation such as azimuth as independent latentvariables. Our method makes no assumptions about the dataset, and can work withsynthetic as well as real images (i.e. unsupervised in true sense). We presentour results, by training the model using the $\mu$-VAE objective\cite{ucar2019bridging} and a dataset combining all images from MNIST, MNISTFashion, CelebA and six categories of ModelNet40. The model is able to learn 3Dshapes and the pose in qood quality and leverages information learned acrossall datasets.

Inverse Graphics: Unsupervised Learning of 3D Shapes from Single Images