Multi-Level Optical Flow Estimation Based On Spatial Partitioning

We address the problem of optical flow estimation between a stereo pair of images suitable for view interpolation applications. In recent years, deep learning based approaches are found to be capable of producing superior optical flow estimation results than that of traditional approaches. However, deep learning approaches generally suffer from large GPU memory constraints and hence their application is limited to low resolution images. Here, we present an approach to accurate optical flow estimation via the state-of-the-art deep learning based approaches that introduces fewer artifacts in view interpolation applications. We confirm the suitability of our approach through both qualitative and quantitative results.