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

Inferring 3D structure of a generic object from a 2D image is a long-standingobjective of computer vision. Conventional approaches either learn completelyfrom CAD-generated synthetic data, which have difficulty in inference from realimages, or generate 2.5D depth image via intrinsic decomposition, which islimited compared to the full 3D reconstruction. One fundamental challenge liesin how to leverage numerous real 2D images without any 3D ground truth. Toaddress this issue, we take an alternative approach with semi-supervisedlearning. That is, for a 2D image of a generic object, we decompose it intolatent representations of category, shape and albedo, lighting and cameraprojection matrix, decode the representations to segmented 3D shape and albedorespectively, and fuse these components to render an image well approximatingthe input image. Using a category-adaptive 3D joint occupancy field (JOF), weshow that the complete shape and albedo modeling enables us to leverage real 2Dimages in both modeling and model fitting. The effectiveness of our approach isdemonstrated through superior 3D reconstruction from a single image, beingeither synthetic or real, and shape segmentation.

Fully Understanding Generic Objects: Modeling, Segmentation, and Reconstruction