Abstract
We present a novel method to integrate multiple 3D scans captured from different viewpoints. Saliency information is used to guide the integration process. The multi-scale saliency of a point is specifically designed to reflect its sensitivity to registration errors. Then scans are partitioned into salient and non-salient regions through an Markov Random Field (MRF) framework where neighbourhood consistency is incorporated to increase the robustness against potential scanning errors. We then develop different schemes to discriminatively integrate points in the two regions. For the points in salient regions which are more sensitive to registration errors, we employ the Iterative Closest Point algorithm to compensate the local registration error and find the correspondences for the integration. For the points in non-salient regions which are less sensitive to registration errors, we integrate them via an efficient and effective point-shifting scheme. A comparative study shows that the proposed method delivers improved surface integration.
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