Zippered polygon meshes from range images
SIGGRAPH '94 Proceedings of the 21st annual conference on Computer graphics and interactive techniques
Industrial inspection and reverse engineering
Computer Vision and Image Understanding
Exploring three-dimensional objects by controlling the point of observation
Exploring three-dimensional objects by controlling the point of observation
SIGGRAPH '96 Proceedings of the 23rd annual conference on Computer graphics and interactive techniques
A volumetric method for building complex models from range images
SIGGRAPH '96 Proceedings of the 23rd annual conference on Computer graphics and interactive techniques
Autonomous Exploration: Driven by Uncertainty
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automated Model Acquisition from Range Images with View Planning
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Consensus Surfaces for Modeling 3D Objects from Multiple Range Images
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Geometry and texture recovery of scenes of large scale
Computer Vision and Image Understanding
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
View Planning for Unknown Indoor Scenes Based on a Cost Benefit Analysis
Proceedings of the 23rd DAGM-Symposium on Pattern Recognition
Advances in Computational Stereo
IEEE Transactions on Pattern Analysis and Machine Intelligence
Integrated image and graphics technologies
Genetic algorithms for positioning and utilizing sensors in synthetically generated landscapes
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Exploration trees on highly complex scenes: A new approach for 3D segmentation
Pattern Recognition
A General Method for Sensor Planning in Multi-Sensor Systems: Extension to Random Occlusion
International Journal of Computer Vision
An occlusion metric for selecting robust camera configurations
Machine Vision and Applications
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
Machine Vision and Applications
A fuzzy model for coverage evaluation of cameras and multi-camera networks
Proceedings of the Fourth ACM/IEEE International Conference on Distributed Smart Cameras
Multi-view scans alignment for 3D spherical mosaicing in large-scale unstructured environments
Computer Vision and Image Understanding
Active vision in robotic systems: A survey of recent developments
International Journal of Robotics Research
Generalized multi-sensor planning
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Modeling Coverage in Camera Networks: A Survey
International Journal of Computer Vision
Pipeline-Architecture Based Real-Time Active-Vision for Human-Action Recognition
Journal of Intelligent and Robotic Systems
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We describe an automated scene modeling system that consists of two components operating in an interleaved fashion: an incremental modeler that builds solid models from range imagery and a sensor planner that analyzes the resulting model and computes the next sensor position. This planning component is target-driven and computes sensor positions using model information about the imaged surfaces and the unexplored space in a scene. The method is shape-independent and uses a continuous-space representation that preserves the accuracy of sensed data. It is able to completely acquire a scene by repeatedly planning sensor positions, utilizing a partial model to determine volumes of visibility for contiguous areas of unexplored scene. These visibility volumes are combined with sensor placement constraints to compute sets of occlusion-free sensor positions that are guaranteed to improve the quality of the model. We show results for the acquisition of a scene that includes multiple, distinct objects with high occlusion.