Automatic Sensor Placement from Vision Task Requirements
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special Issue on Industrial Machine Vision and Computer Vision Technology:8MPart
A volumetric method for building complex models from range images
SIGGRAPH '96 Proceedings of the 23rd annual conference on Computer graphics and interactive techniques
A Solution to the Next Best View Problem for Automated Surface Acquisition
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Information Theoretic Approach for Next Best View Planning in 3-D Reconstruction
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
Pose estimation and map building with a Time-Of-Flight-camera for robot navigation
International Journal of Intelligent Systems Technologies and Applications
Information entropy-based viewpoint planning for 3-D object reconstruction
IEEE Transactions on Robotics
Vision sensor planning for 3-D model acquisition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Solving the next best view (NBV) problem is an important task for automated 3D reconstruction. An NBV algorithm provides sensor positions, from which maximal information gain about the measurement object during the next scan can be expected. With no or limited information available during the first views, automatic data driven view planning performs suboptimal. In order to overcome these inefficiencies during startup phase, we examined the use of time-of-flight (TOF) camera data to improve view planning. The additional low resolution 3D information, gathered during sensor movement, allows to plan even the first scans customized to previously unknown objects. Measurement examples using a robot mounted fringe projection stereo 3D scanner with a TOF camera are presented.