Multiscale gigapixel video: A cross resolution image matching and warping approach
We present a multi-scale camera array to capture and synthesize gigapixel videos in an efficient way. Our acquisition setup contains a reference camera with a short-focus lens to get a large field-of-view video and a number of unstructured long-focus cameras to capture local-view details. Based on this new design, we propose an iterative feature matching and image warping method to independently warp each local-view video to the reference video. The key feature of the proposed algorithm is its robustness to and high accuracy for the huge resolution gap (more than 8x resolution gap between the reference and the local-view videos), camera parallaxes, complex scene appearances and color inconsistency among cameras. Experimental results show that the proposed multi-scale camera array and cross resolution video warping scheme is capable of generating seamless gigapixel video without the need of camera calibration and large overlapping area constraints between the local-view cameras.