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

Xiaoqiang Zhang, Yanning Zhang, Tao Yang, Ting Chen, Yee-Hong Yang

Abstract: Recently, researchers are interested in mobile device based computer vision applications. An accurate visual pose estimation is a common and important subtask. To quantitatively 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 difficult, if not impossible, to quantitatively evaluate the estimated visual poses. In this paper, a novel and easy-to-implement experimental setup is proposed to generate ground truth visual pose data for handheld tablet-PC. The tablet-PC screen is leveraged to display a calibration pattern every time the on-board camera captures 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 parameter calibration and pose accuracy verification. Extensive experimental results with quantitative analysis demonstrate the accuracy and the generality of our tool.