A Mechanism of Automatic 3D Object Modeling
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Occlusions as a Guide for Planning the Next View
IEEE Transactions on Pattern Analysis and Machine Intelligence
View planning for automated three-dimensional object reconstruction and inspection
ACM Computing Surveys (CSUR)
Solid model acquistion from range imagery
Solid model acquistion from range imagery
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This paper addresses the view planning problem for the digitization of 3D objects without prior knowledge on their shape and presents a novel surface approach for the Next Best View (NBV) computation. The proposed method uses the concept of Mass Vector Chains (MVC) to define the global orientation of the scanned part. All of the viewpoints satisfying an orientation constraint are clustered using the Mean Shift technique to construct a first set of candidates for the NBV. Then, a weight is assigned to each mode according to the elementary orientations of its different descriptors. The NBV is chosen among the modes with the highest weights and which comply with the robotics constraints. Eventually, our method is generic since it is applicable to all kinds of scanners. Experiments applying a digitization cell demonstrate the feasibility and the efficiency of the approach which leads to an intuitive and fast 3D acquisition while moving efficiently the ranging device.