A Mechanism of Automatic 3D Object Modeling
IEEE Transactions on Pattern Analysis and Machine Intelligence
Planning for complete sensor coverage in inspection
Computer Vision and Image Understanding
Autonomous Exploration: Driven by Uncertainty
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)
Planning the Next View Using the Max-Min Principle
CAIP '93 Proceedings of the 5th International Conference on Computer Analysis of Images and Patterns
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1
A Sensor-Based Solution to the Next Best View Problem
ICPR '96 Proceedings of the 1996 International Conference on Pattern Recognition (ICPR '96) Volume I - Volume 7270
Solid model acquistion from range imagery
Solid model acquistion from range imagery
Efficient Constraint Evaluation Algorithms for Hierarchical Next-Best-View Planning
3DPVT '06 Proceedings of the Third International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT'06)
An automation system for industrial 3-D laser digitizing
3DIM'99 Proceedings of the 2nd international conference on 3-D digital imaging and modeling
Active vision in robotic systems: A survey of recent developments
International Journal of Robotics Research
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Sensor planning is a critical issue since a typical 3-D sensor can only sample a portion of an object at a single viewpoint. The primary focus of the research described in this paper is to propose a new method of creating a complete model of free-form surface object from multiple range images acquired by a scan sensor at different space poses. Candidates for the best-next-view position are determined by detecting and measuring occlusions to the camera's view in an image. Ultimately, the candidate which obtains maximum visible space volume is selected as the Next-best-view position. The experimental results show that the method is effective in practical implementation.