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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

We present a new learning-based framework S-3D-RCNN that can recover accurateobject orientation in SO(3) and simultaneously predict implicit shapes foroutdoor rigid objects from stereo RGB images. In contrast to previous studiesthat map local appearance to observation angles, we explore a progressiveapproach by extracting meaningful Intermediate Geometrical Representations(IGRs) to estimate egocentric object orientation. This approach features a deepmodel that transforms perceived intensities to object part coordinates, whichare mapped to a 3D representation encoding object orientation in the cameracoordinate system. To enable implicit shape estimation, the IGRs are furtherextended to model visible object surface with a point-based representation andexplicitly addresses the unseen surface hallucination problem. Extensiveexperiments validate the effectiveness of the proposed IGRs and S-3D-RCNNachieves superior 3D scene understanding performance using existing andproposed new metrics on the KITTI benchmark. Code and pre-trained models willbe available at this https URL.

Joint stereo 3D object detection and implicit surface reconstruction