A Method for Registration of 3-D Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
Surface reconstruction from unorganized points
SIGGRAPH '92 Proceedings of the 19th annual conference on Computer graphics and interactive techniques
Zippered polygon meshes from range images
SIGGRAPH '94 Proceedings of the 21st annual conference on Computer graphics and interactive techniques
A robust method for registration and segmentation of multiple range images
Computer Vision and Image Understanding
Towards a General Multi-View Registration Technique
IEEE Transactions on Pattern Analysis and Machine Intelligence
Rigid, affine and locally affine registration of free-form surfaces
International Journal of Computer Vision
A volumetric method for building complex models from range images
SIGGRAPH '96 Proceedings of the 23rd annual conference on Computer graphics and interactive techniques
Principal Component Analysis with Missing Data and Its Application to Polyhedral Object Modeling
IEEE Transactions on Pattern Analysis and Machine Intelligence
Registration of Multiple Point Sets
ICPR '96 Proceedings of the 13th International Conference on Pattern Recognition - Volume 2
Special issue on registration and fusion of range images
Computer Vision and Image Understanding - Registration and fusion of range images
A volumetric approach for interactive 3D modeling
Computer Vision and Image Understanding
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Registration and integration of measured shape data of real objects are important for 3-D modeling for computer graphics and computer-aided design. We propose a new algorithm that solves registration and integration of multiple range images in a unified framework. The closest point of an object from a general 3-D point can be determined uniquely. If we have range images of an object and the images are registered correctly, the closest points found on the range images should be matched except for the case where the part is not measured. We register multiple range images by minimizing the error function determined by the variance of the closest points from every point in the 3-D volume surrounding the object. We introduce scale factors to control the detail of the shape to be considered. The result contains the information of the closest point on the integrated shape, from which we can determine the distance and surface normal to the object. We experimented this method on synthetic and measured range images.