The representation, recognition, and locating of 3-d objects
International Journal of Robotics Research
Stochastic Analysis of Stereo Quantization Error
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Object modelling by registration of multiple range images
Image and Vision Computing - Special issue: range image understanding
Model-based object recognition in dense-range images—a review
ACM Computing Surveys (CSUR)
Zippered polygon meshes from range images
SIGGRAPH '94 Proceedings of the 21st annual conference on Computer graphics and interactive techniques
Iterative point matching for registration of free-form curves and surfaces
International Journal of Computer Vision
A General Surface Approach to the Integration of a Set of Range Views
IEEE Transactions on Pattern Analysis and Machine Intelligence
Registering range views of multipart objects
Computer Vision and Image Understanding
A robust method for registration and segmentation of multiple range images
Computer Vision and Image Understanding
Description of complex objects from multiple range images using an inflating balloon model
Computer Vision and Image Understanding
Registration and Integration of Multiple Object Views for 3D Model Construction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Reliable Surface Reconstructiuon from Multiple Range Images
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
Special issue on registration and fusion of range images
Computer Vision and Image Understanding - Registration and fusion of range images
Computer Vision and Image Understanding
Multi-sensor calibration through iterative registration and fusion
Computer-Aided Design
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This paper presents a new method for fine registration of two range images from different viewpoints that have been roughly registered. Our method deals with the properties of the measurement error of the range image data. The error distribution is different for each point in the image and is usually dependent on both the viewing direction and the distance to the object surface. We find the best transformation of two range images to align each measured point and reconstruct 3D total object shape by taking such properties of the measurement error into account. The position of each measured point is corrected according to the variance and the extent of the distribution of its measurement error. The best transformation is selected by the evaluation of the effectiveness of this correction of every measured point. The experiments showed that our method produced better results than the conventional ICP method.