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

One of the key criticisms of deep learning is that large amounts of expensiveand difficult-to-acquire training data are required in order to train modelswith high performance and good generalization capabilities. Focusing on thetask of monocular camera pose estimation via scene coordinate regression (SCR),we describe a novel method, Domain Adaptation of Networks for Camera poseEstimation (DANCE), which enables the training of models without access to anylabels on the target task. DANCE requires unlabeled images (without knownposes, ordering, or scene coordinate labels) and a 3D representation of thespace (e.g., a scanned point cloud), both of which can be captured with minimaleffort using off-the-shelf commodity hardware. DANCE renders labeled syntheticimages from the 3D model, and bridges the inevitable domain gap betweensynthetic and real images by applying unsupervised image-level domainadaptation techniques (unpaired image-to-image translation). When tested onreal images, the SCR model trained with DANCE achieved comparable performanceto its fully supervised counterpart (in both cases using PnP-RANSAC for finalpose estimation) at a fraction of the cost. Our code and dataset are availableat https://github.com/JackLangerman/dance

Domain Adaptation of Networks for Camera Pose Estimation: Learning Camera Pose Estimation Without Pose Labels