Scale-Based Description and Recognition of Planar Curves and Two-Dimensional Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Assembling virtual pots from 3D measurements of their fragments
Proceedings of the 2001 conference on Virtual reality, archeology, and cultural heritage
A Multiscale Method for the Reassembly of Two-Dimensional Fragmented Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
3-D Curve Matching Using Splines
ECCV '90 Proceedings of the First European Conference on Computer Vision
Reassembling fractured objects by geometric matching
ACM SIGGRAPH 2006 Papers
Multiview registration for large data sets
3DIM'99 Proceedings of the 2nd international conference on 3-D digital imaging and modeling
Multiview registration of 3D scenes by minimizing error between coordinate frames
IEEE Transactions on Pattern Analysis and Machine Intelligence
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We introduce a novel reassembly method for fragmented, thin objects that uses minimal user interaction. Unlike past methods, we do not make any restrictive assumptions about the geometry or texture of the object. To do so, we exploit the geometric and photometric similarity along and across the boundaries of matching fragments, and leverage user feedback to tackle the otherwise ill-posed problem. We begin by encoding the scale variability of each fragment's boundary contour in a multi-channel, 2D representation. Using this multi-channel boundary contour representation, we identify matching sub-contours via 2D partial image alignment. We then align the fragments by minimizing the distance between their adjoining regions while simultaneously ensuring geometric continuity across them. The configuration of the fragments as they are incrementally matched and aligned form a graph structure. By detecting cycles in this graph, we identify subsets of fragments with dependent alignments. We then minimize the error within the subsets to achieve a globally optimal alignment. Using ceramic pottery as the driving example, we demonstrate the accuracy and efficiency of our method on six real-world datasets.