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.

翻译：运动变形的工程要么忽略了深度依赖模糊的影响,要么假设多层场景,其中每个层以前方和平行平面的形式建模。在这项工作中,我们考虑三维场景的情况,其结构是片状平面结构,即一个可建模的场景,可建模为多平面的组合,带有任意取向。我们首先提出一种方法,从一个运动模糊的观察中估计平板场景的正常性。然后我们制定一种自动回收飞机数的算法,每架飞机对应的参数,以及多平面3D场一幅模糊的图象的摄影机动作。最后,我们提出一种首选办法,通过采用基于我们发现结果的交替最小化框架来恢复平面的平面和潜在图像。对合成和实际数据的实验表明,我们拟议的方法取得了最新的结果。