Augmented Exhibitions Using Natural Features

In this paper, we propose an augmented reality application for museum exhibitions using natural features instead of calibrated fiducials to recognize paintings and recover their pose. The proposed system utilizes an adapted Multiple View Kernel Projection (MVKP), which combines a multiple view training stage for geometric invariance and kernel projection based on Walsh-Hadamard kernels for feature description. We demonstrate that its real-time performance and robustness to lighting and viewpoint changes make it ideal for AR applications like AR exhibition systems. After obtaining the painting’s index, the system retrieves related information from a remote server and displays it as virtual content overlaid on top of the painting image. Experimental results on a real-world painting exhibition have demonstrated the effectiveness of the proposed approach.