A Method for Registration of 3-D Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
A Mechanism of Automatic 3D Object Modeling
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Solution to the Next Best View Problem for Automated Surface Acquisition
IEEE Transactions on Pattern Analysis and Machine Intelligence
An improved illumination model for shaded display
Communications of the ACM
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital Image Processing
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)
Planning the Next View Using the Max-Min Principle
CAIP '93 Proceedings of the 5th International Conference on Computer Analysis of Images and Patterns
Solid model acquistion from range imagery
Solid model acquistion from range imagery
Constraints-based motion planning for an automatic, flexible laser scanning robotized platform
AQTR '08 Proceedings of the 2008 IEEE International Conference on Automation, Quality and Testing, Robotics - Volume 02
Active Sensor Planning for Multiview Vision Tasks
Active Sensor Planning for Multiview Vision Tasks
Online Next-Best-View Planning for Accuracy Optimization Using an Extended E-Criterion
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
A next-best-view system for autonomous 3-D object reconstruction
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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Our goal is to develop a complete and automatic scanning strategy with minimum prior information about the object shape. We aim to establish a methodology for the automation of the 3D digitization process. The paper presents a novel approach to determine the Next Best View (NBV) for an efficient reconstruction of highly accurate 3D models. Our method is based on the classification of the acquired surfaces into Well Visible and Barely Visible combined with a best view selection algorithm based on mean shift, which avoids unreachable positions. Our approach is applicable to all kinds of range sensors. To prove the efficiency and the robustness of our method, test objects are first scanned manually by experts in 3D digitization from the VECTEO company. The comparison of results between manual and automatic scanning shows that our method is very efficient and faster than trained experts. The 3D models of the different objects are obtained with a strongly reduced number of acquisitions while moving efficiently the ranging device. The obtained results prove the effectiveness and the versatility of our 3D reconstruction approach for industrial applications.