Chair for Computer Aided Medical Procedures & Augmented Reality
Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality

Learning and Understanding in 3D Point Clouds (Video1)

We present a revised pipe-line of the existing 3D object detection and pose estimation framework based on point pair feature matching. In particular, we propose to couple the object detection with a coarse-to-fine segmentation, where each segment is subject to disjoint pose estimation. During matching, we apply a weighted Hough voting and an interpolated recovery of pose parameters. Finally, all the generated hypotheses are tested via an occlusion-aware ranking and sorted. We argue that such a combined pipeline simultaneously boosts the detection rate and reduces the complexity, while improving the accuracy of the resulting pose.
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Edit | Attach | Refresh | Diffs | More | Revision r1.3 - 14 May 2008 - 18:43 - LejingWang

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