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

Camera calibration is an essential first step in setting up 3D ComputerVision systems. Commonly used parametric camera models are limited to a fewdegrees of freedom and thus often do not optimally fit to complex real lensdistortion. In contrast, generic camera models allow for very accuratecalibration due to their flexibility. Despite this, they have seen little usein practice. In this paper, we argue that this should change. We propose acalibration pipeline for generic models that is fully automated, easy to use,and can act as a drop-in replacement for parametric calibration, with a focuson accuracy. We compare our results to parametric calibrations. Consideringstereo depth estimation and camera pose estimation as examples, we show thatthe calibration error acts as a bias on the results. We thus argue that incontrast to current common practice, generic models should be preferred overparametric ones whenever possible. To facilitate this, we released ourcalibration pipeline at https://github.com/puzzlepaint/camera_calibration,making both easy-to-use and accurate camera calibration available to everyone.

Why Having 10,000 Parameters in Your Camera Model is Better Than Twelve