Layered Scene Decomposition via the Occlusion-CRF
Layered Scene Decomposition via the Occlusion-CRF
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DOI：
10.1109/cvpr.2016.25
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发表时间：
2016-06
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影响因子：
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通讯作者：
Chen Liu;Pushmeet Kohli;Yasutaka Furukawa
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文献类型：
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作者：
Chen Liu;Pushmeet Kohli;Yasutaka Furukawa
本文讨论了如何感知任意给定RGBD图像中所描绘的场景的隐藏或遮挡的几何形状这一挑战性问题。与其他图像标记问题不同，例如图像分割，每个像素需要被分配一个标签，分层分解需要为像素分配多个标签。我们提出了一种新的“遮挡-CRF”模型，该模型允许集成复杂的先验知识来规则化解空间，并实现层分解的自动推理。我们使用Fusion Move算法的推广来在模型上执行最大后验(MAP)推理，该模型可以处理表示每个像素的多个曲面分配所需的大标签集。我们在大量室内杂波场景的RGBD图像上对提出的模型和推理算法进行了评估。我们的实验表明，我们的模型不仅能够解释遮挡，而且还能够自动修复遮挡/不可见的表面。
This paper addresses the challenging problem of perceiving the hidden or occluded geometry of the scene depicted in any given RGBD image. Unlike other image labeling problems such as image segmentation where each pixel needs to be assigned a single label, layered decomposition requires us to assign multiple labels to pixels. We propose a novel "Occlusion-CRF" model that allows for the integration of sophisticated priors to regularize the solution space and enables the automatic inference of the layer decomposition. We use a generalization of the Fusion Move algorithm to perform Maximum a Posterior (MAP) inference on the model that can handle the large label sets needed to represent multiple surface assignments to each pixel. We have evaluated the proposed model and the inference algorithm on many RGBD images of cluttered indoor scenes. Our experiments show that not only is our model able to explain occlusions but it also enables automatic inpainting of occluded/ invisible surfaces.