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Abstract
We present an end-to-end learned system for fusing multiple misaligned photographs of the same scene into a chosen target view. We demonstrate three use cases: 1) color transfer for inferring color for a monochrome view, 2) HDR fusion for merging misaligned bracketed exposures, and 3) detail transfer for reprojecting a high definition image to the point of view of an affordable VR180-camera. While the system can be trained end-to-end, it consists of three distinct steps: feature extraction, image warping and fusion. We present a novel cascaded feature extraction method that enables us to synergetically learn optical flow at different resolution levels. We show that this significantly improves the network’s ability to learn large disparities. Finally, we demonstrate that our alignment architecture outperforms a state-of-the art optical flow network on the image warping task when both systems are trained in an identical manner.
