A Viewpoint Planning Strategy for Determining True Angles on Polyhedral Objects by Camera Alignment
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)
Transinformation for Active Object Recognition
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Calibration and registration for precise surface reconstruction with Time-Of-Flight cameras
International Journal of Intelligent Systems Technologies and Applications
Make3D: depth perception from a single still image
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
Three-dimensional mapping with time-of-flight cameras
Journal of Field Robotics - Three-Dimensional Mapping, Part 2
Active Sensor Planning for Multiview Vision Tasks
Active Sensor Planning for Multiview Vision Tasks
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Active view planning for gathering data from an unexplored 3D complex scenario is a hard and still open problem in the computer vision community. In this paper, we present a general task-oriented approach based on an information-gain maximization that easily deals with such a problem. Our approach consists of ranking a given set of possible actions, based on their task-related gains, and then executing the best-ranked action to move the required sensor. An example of how our approach behaves is demonstrated by applying it over 3D raw data for real-time volume modelling of complex-shaped objects. Our setting includes a calibrated 3D time-of-flight (ToF) camera mounted on a 7 degrees of freedom (DoF) robotic arm. Noise in the sensor data acquisition, which is too often ignored, is here explicitly taken into account by computing an uncertainty matrix for each point, and refining this matrix each time the point is seen again. Results show that, by always choosing the most informative view, a complete model of a 3D free-form object is acquired and also that our method achieves a good compromise between speed and precision.