Robot vision
3-D multiview object representation for model-based object recognition
Pattern Recognition
The design and analysis of spatial data structures
The design and analysis of spatial data structures
Computing the Aspect Graph for Line Drawings of Polyhedral Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object modelling by registration of multiple range images
Image and Vision Computing - Special issue: range image understanding
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
Active modeling of 3-D objects: planning on the next best pose (NBP) for acquiring range images
NRC '97 Proceedings of the International Conference on Recent Advances in 3-D Digital Imaging and Modeling
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
View planning for automated three-dimensional object reconstruction and inspection
ACM Computing Surveys (CSUR)
A tree-structured perception approach for robot operations in modeling of unknown targets
ISCGAV'04 Proceedings of the 4th WSEAS International Conference on Signal Processing, Computational Geometry & Artificial Vision
A tree-structured perception approach for robot operations in modeling of unknown targets
AIC'04 Proceedings of the 4th WSEAS International Conference on Applied Informatics and Communications
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Viewpoint planning plays an important role in automatic 3-D model generation. In a previous paper [18], we have proposed a viewpoint planning method to determine the next-best-viewpoint (NBV) for incremental model construction. Based on a current partial model, this algorithm provides quantitative evaluations on the suitability of viewpoints as the NBV. Since the evaluation is performed for all potential viewpoints, the NBV planning is very time-consuming. In this paper, we present a new method of discretizing a spherical view space by a look-up array which will highly facilitate the NBV evaluations. Two main issues are addressed: 1) a uniform tessellation of the spherical space and its mapping onto the 2-D array; 2) incremental updating computations for evaluating viewpoints as the NBV. The efficiency of the method is verified by algorithmic analyses and experiments using a real modeling system.