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
Using occluding contours for 3-D object modeling
ECCV 90 Proceedings of the first european conference on Computer vision
Artificial Intelligence
Visual exploration of free-space
Active vision
International Journal of Robotics Research
Structure From Controlled Motion
IEEE Transactions on Pattern Analysis and Machine Intelligence
X Vision: a portable substrate for real-time vision applications
Computer Vision and Image Understanding
Occlusions as a Guide for Planning the Next View
IEEE Transactions on Pattern Analysis and Machine Intelligence
Visual Data Fusion for Objects Localization by Active Vision
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
3D Complex Scenes Segmentation from a Single Range Image Using Virtual Exploration
IBERAMIA 2002 Proceedings of the 8th Ibero-American Conference on AI: Advances in Artificial Intelligence
Exploration trees on highly complex scenes: A new approach for 3D segmentation
Pattern Recognition
Evolutionary computation for sensor planning: the task distribution plan
EURASIP Journal on Applied Signal Processing
Active-Vision System Reconfiguration for Form Recognition in the Presence of Dynamic Obstacles
AMDO '08 Proceedings of the 5th international conference on Articulated Motion and Deformable Objects
Developing visual sensing strategies through next best view planning
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Active vision in robotic systems: A survey of recent developments
International Journal of Robotics Research
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This paper deals with the 3D structure estimation and exploration of static scenes using active vision. Our method is based on the structure from controlled motion approach that constrains camera motions to obtain an optimal estimation of the 3D structure of a geometrical primitive. Since this approach involves to gaze on the considered primitive, we have developed perceptual strategies able to perform a succession of robust estimations. This leads to a gaze planning strategy that mainly uses a representation of known and unknown areas as a basis for selecting viewpoints. This approach ensures a reconstruction as complete as possible of the scene.