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Motivated by the important archaeological application of exploring culturalheritage objects, in this paper we study the challenging problem ofautomatically segmenting curve structures that are very weakly stamped orcarved on an object surface in the form of a highly noisy depth map. Differentfrom most classical low-level image segmentation methods that are known to bevery sensitive to the noise and occlusions, we propose a new supervisedlearning algorithm based on Convolutional Neural Network (CNN) to implicitlylearn and utilize more curve geometry and pattern information for addressingthis challenging problem. More specifically, we first propose a FullyConvolutional Network (FCN) to estimate the skeleton of curve structures and ateach skeleton pixel, a scale value is estimated to reflect the local curvewidth. Then we propose a dense prediction network to refine the estimated curveskeletons. Based on the estimated scale values, we finally develop an adaptivethresholding algorithm to achieve the final segmentation of curve structures.In the experiment, we validate the performance of the proposed method on adataset of depth images scanned from unearthed pottery sherds dating to theWoodland period of Southeastern North America.

Curve-Structure Segmentation from Depth Maps: A CNN-based Approach and Its Application to Exploring Cultural Heritage Objects