Planar Geometry and Image Recovery from Motion-Blur
This addresses motion deblurring for 3D scenes with arbitrary planar orientations, an incremental improvement over methods that ignore depth-dependent blur or assume fronto-parallel planes.
The paper tackles the problem of recovering planar geometry and latent images from motion-blurred observations in 3D scenes with piecewise planar structure, achieving state-of-the-art results in experiments on synthetic and real data.
Existing works on motion deblurring either ignore the effects of depth-dependent blur or work with the assumption of a multi-layered scene wherein each layer is modeled in the form of fronto-parallel plane. In this work, we consider the case of 3D scenes with piecewise planar structure i.e., a scene that can be modeled as a combination of multiple planes with arbitrary orientations. We first propose an approach for estimation of normal of a planar scene from a single motion blurred observation. We then develop an algorithm for automatic recovery of number of planes, the parameters corresponding to each plane, and camera motion from a single motion blurred image of a multiplanar 3D scene. Finally, we propose a first-of-its-kind approach to recover the planar geometry and latent image of the scene by adopting an alternating minimization framework built on our findings. Experiments on synthetic and real data reveal that our proposed method achieves state-of-the-art results.