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
Reassembling fractured objects by geometric matching
ACM SIGGRAPH 2006 Papers
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Feature-based Part Retrieval for Interactive 3D Reassembly
WACV '07 Proceedings of the Eighth IEEE Workshop on Applications of Computer Vision
Multi-feature matching of fresco fragments
ACM SIGGRAPH Asia 2010 papers
Contour-shape based reconstruction of fragmented, 1600 BC wallpaintings
IEEE Transactions on Signal Processing
Analyzing fracture patterns in theran wall paintings
VAST'10 Proceedings of the 11th International conference on Virtual Reality, Archaeology and Cultural Heritage
Example-Based Fractured Appearance
Computer Graphics Forum
Field-guided registration for feature-conforming shape composition
ACM Transactions on Graphics (TOG) - Proceedings of ACM SIGGRAPH Asia 2012
A benchmark for surface reconstruction
ACM Transactions on Graphics (TOG)
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One of the main problems faced during reconstruction of fractured archaeological artifacts is sorting through a large number of candidate matches between fragments to find the relatively few that are correct. Previous computer methods for this task provided scoring functions based on a variety of properties of potential matches, including color and geometric compatibility across fracture surfaces. However, they usually consider only one or at most a few properties at once, and therefore provide match predictions with very low precision. In this article, we investigate a machine learning approach that computes the probability that a match is correct based on the combination of many features. We explore this machine learning approach for ranking matches in three different sets of fresco fragments, finding that classifiers based on many match properties can be significantly more effective at ranking proposed matches than scores based on any single property alone. Our results suggest that it is possible to train a classifier on match properties in one dataset and then use it to rank predicted matches in another dataset effectively. We believe that this approach could be helpful in a variety of cultural heritage reconstruction systems.