Stereo Unstructured Magnification: Multiple Homography Image for View Synthesis
Qi Zhang, Xin Huang, Ying Feng, Xue Wang, Hongdong Li and, Qing Wang

TL;DR
This paper introduces a novel multiple homography image (MHI) representation and a two-stage neural network for view synthesis from stereo images with rotations, outperforming existing methods especially in rotational scenarios.
Contribution
The paper proposes the MHI representation and a two-stage network to generalize view synthesis to unstructured views with rotations, addressing depth-dependency issues.
Findings
Achieves superior qualitative view synthesis results.
Outperforms state-of-the-art methods quantitatively.
Effective in scenarios with camera rotations.
Abstract
This paper studies the problem of view synthesis with certain amount of rotations from a pair of images, what we called stereo unstructured magnification. While the multi-plane image representation is well suited for view synthesis with depth invariant, how to generalize it to unstructured views remains a significant challenge. This is primarily due to the depth-dependency caused by camera frontal parallel representation. Here we propose a novel multiple homography image (MHI) representation, comprising of a set of scene planes with fixed normals and distances. A two-stage network is developed for novel view synthesis. Stage-1 is an MHI reconstruction module that predicts the MHIs and composites layered multi-normal images along the normal direction. Stage-2 is a normal-blending module to find blending weights. We also derive an angle-based cost to guide the blending of multi-normal…
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Taxonomy
TopicsAdvanced Vision and Imaging · Advanced Image and Video Retrieval Techniques · Robotics and Sensor-Based Localization
