A Deep Learning Approach to Assist in Pottery Reconstruction from Its Sherds

Pottery is one of the most common and abundant types of human remains found in archaeological contexts. The analysis of archaeological pottery involves the reconstruction of pottery vessels from their sherds, which represents a laborious and repetitive task. In this work, we investigate a deep learning-based approach to make that process more efficient, accurate, and fast. In that regard, given a sherd’s digital point cloud in a standard, so-called canonical position, the proposed method predicts the geometric transformation, which moves the sherd to its expected normalized position relative to the vessel’s coordinate system. Among the main components of the proposed method, a pair of deep 1D convolutional neural networks trained to predict the 3D Euclidean transformation parameters stands out. Herein, rotation and translation components are treated as independent problems, so while the first network is dedicated to predicting translation moments, the other infers the rotation parameters. In practical applications, once a vessel’s shape is identified, the networks can be trained to predict the target transformation parameter values. Thus, given a 3D model of a complete vessel, it may be virtually broken down countless times for the production of sufficient data to meet deep neural network training demands. In addition to overcoming the scarcity of real sherd data, given a virtual sherd in its original position, that procedure provides paired canonical and normalized point clouds, as well as the target Euclidean transformation. The herein proposed 1D convolutional neural network architecture, the so-called PotNet, was inspired by the PointNet architecture. While PointNet was motivated by 3D point cloud classification and segmentation applications, PotNet was designed to perform non-linear regressions. The method is able to provide an initial estimate for the correct position of a sherd, reducing the complexity of the problem of fitting candidate pairs of sherds, which could be then carried out by a classical adjustment method like ICP, for instance. Experiments using three distinct real vessels were carried out, and the reported results suggest that the proposed method can be successfully used for aiding pottery reconstruction.