We propose a method that extends a given depth image into regions in 3D that are not visible from the point of view of the camera. The algorithm detects repeated 3D structures in the visible scene and suggests a set of 3D extension hypotheses, which are then combined together through a global 3D MRF discrete optimization. The recovered global 3D surface is consistent with both the input depth map and the hypotheses.
A key component of this work is a novel 3D template matcher that is used to detect repeated 3D structure in the scene and to suggest the hypotheses. A unique property of this matcher is that it can handle depth uncertainty. This is crucial because the matcher is required to “peek around the corner”, as it operates at the boundaries of the visible 3D scene where depth information is missing. The proposed matcher is fast and is guaranteed to find an approximation to the globally optimal solution.
We demonstrate on real-world data that our algorithm is capable of completing a full 3D scene from a single depth image and can synthesize a full depth map from a novel viewpoint of the scene. In addition, we report results on an extensive synthetic set of 3D shapes, which allows us to evaluate the method both qualitatively and quantitatively.