Stereo Unstructured Magnification: Multiple Homography Image for View Synthesis
This work addresses the problem of synthesizing novel views from stereo images with rotations for computer vision applications, representing an incremental improvement over existing multi-plane image methods.
The paper tackles the challenge of view synthesis with camera rotations by proposing a multiple homography image (MHI) representation, achieving superior performance compared to state-of-the-art methods, particularly in handling rotational cases.
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 images by exploiting per-normal geometry. Compared with the state-of-the-art methods, our method achieves superior performance for view synthesis qualitatively and quantitatively, especially for cases when the cameras undergo rotations.