Automatic segmentation of archaeological fragments with relief patterns using convolutional neural networks

The recent commodification of high-quality 3D scanners is leading to the possibility of capturing models of archaeological finds and automatically recognizing their surface reliefs. We present our advancements in this field using Convolutional Neural Networks (CNNs) to segment and classify the region around a vertex in a robust way. The network is trained with high-resolution views of the 3D models captured at different angles. The views represent both the model with its original textures and a colorization of the patches according to the value of the Shape Index (SI) in their vertices. The SI encodes local surface variations and we exploit the colorization of the model driven by the SI to generate other view and enrich the dataset. Our method has been validated on a relief recognition benchmark on archaeological fragments proposed within the SHape REtrieval Contest (SHREC) 2018.