An easy-to-implement Benchmarking Tool for Mobile Tablet-PC Visual Pose Estimation

Recently, researchers are interested in mobile device based computer vision applications. An accurate visual pose es-timation is a common and important subtask. To quan-titatively evaluate the accuracy, ground truth visual pose datasets are needed. However, the lack of inexpensive and easy benchmarking tool for mobile device based visual poses estimation makes it di-cult, if not impossible, to quantita-tively evaluate the estimated visual poses. In this paper, a novel and easy-to-implement experimental setup is pro-posed to generate ground truth visual pose data for hand-held tablet-PC. The tablet-PC screen is leveraged to display a calibration pattern every time the on-board camera cap-tures an image. The tablet-PC screen image is captured by another camera and is used to estimate the visual pose of the tablet-PC. An experimental environment is setup for param-eter calibration and pose accuracy veri-cation. Extensive experimental results with quantitative analysis demonstrate the accuracy and the generality of our tool.