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A Contour Completion Model for Augmenting Surface Reconstructions
Nathan Silberman1, Lior Shapira2, Ran Gal2, and Pushmeet Kohli3
1Courant Institute, New York University, USA
2Microsoft Research, USA
3Microsoft Research, Cambridge, UK
Abstract. The availability of commodity depth sensors such as Kinect has enabled development of methods which can densely reconstruct arbitrary scenes. While the results of these methods are accurate and visually appealing, they are quite often incomplete. This is either due to the fact that only part of the space was visible during the data capture process or due to the surfaces being occluded by other objects in the scene. In this paper, we address the problem of completing and refining such reconstructions. We propose a method for scene completion that can infer the layout of the complete room and the full extent of partially occluded objects. We propose a new probabilistic model, Contour Completion Random Fields, that allows us to complete the boundaries of occluded surfaces. We evaluate our method on synthetic and real world reconstructions of 3D scenes and show that it quantitatively and qualitatively outperforms standard methods. We created a large dataset of partial and complete reconstructions which we will make available to the community as a benchmark for the scene completion task. Finally, we demonstrate the practical utility of our algorithm via an augmented-reality application where objects interact with the completed reconstructions inferred by our method.
Keywords: 3D Reconstruction, Scene Completion, Surface Reconstruction, Contour Completion
LNCS 8691, p. 488 ff.
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© Springer International Publishing Switzerland 2014