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

Traditional approaches for learning 3D object categories have beenpredominantly trained and evaluated on synthetic datasets due to theunavailability of real 3D-annotated category-centric data. Our main goal is tofacilitate advances in this field by collecting real-world data in a magnitudesimilar to the existing synthetic counterparts. The principal contribution ofthis work is thus a large-scale dataset, called Common Objects in 3D, with realmulti-view images of object categories annotated with camera poses and groundtruth 3D point clouds. The dataset contains a total of 1.5 million frames fromnearly 19,000 videos capturing objects from 50 MS-COCO categories and, as such,it is significantly larger than alternatives both in terms of the number ofcategories and objects. We exploit this new dataset to conduct one of the firstlarge-scale "in-the-wild" evaluations of several new-view-synthesis andcategory-centric 3D reconstruction methods. Finally, we contribute NerFormer -a novel neural rendering method that leverages the powerful Transformer toreconstruct an object given a small number of its views. The CO3D dataset isavailable at https://github.com/facebookresearch/co3d .

Common Objects in 3D: Large-Scale Learning and Evaluation of Real-life 3D Category Reconstruction