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

We present Im2Pano3D, a convolutional neural network that generates a denseprediction of 3D structure and a probability distribution of semantic labelsfor a full 360 panoramic view of an indoor scene when given only a partialobservation (<= 50%) in the form of an RGB-D image. To make this possible,Im2Pano3D leverages strong contextual priors learned from large-scale syntheticand real-world indoor scenes. To ease the prediction of 3D structure, wepropose to parameterize 3D surfaces with their plane equations and train themodel to predict these parameters directly. To provide meaningful trainingsupervision, we use multiple loss functions that consider both pixel levelaccuracy and global context consistency. Experiments demon- strate thatIm2Pano3D is able to predict the semantics and 3D structure of the unobservedscene with more than 56% pixel accuracy and less than 0.52m average distanceerror, which is significantly better than alternative approaches.

Im2Pano3D: Extrapolating 360 Structure and Semantics Beyond the Field of View