The 3D-Pitoti Dataset: A Dataset for high-resolution 3D Surface Segmentation

The development of powerful 3D scanning hardware and recon-
struction algorithms has strongly promoted the generation of 3D
surface reconstructions in different domains. An area of special
interest for such 3D reconstructions is the cultural heritage do-
main, where surface reconstructions are generated to digitally pre-
serve historical artifacts. While reconstruction quality nowadays
is sufficient in many cases, the robust analysis (e.g. segmentation,
matching, and classification) of reconstructed 3D data is still an
open topic. In this paper, we target the automatic segmentation of
high-resolution 3D surface reconstructions of petroglyphs. To foster
research in this field, we introduce a fully annotated, large-scale 3D
surface dataset including high-resolution meshes, depth maps and
point clouds as a novel benchmark dataset, which we make publicly
available. Additionally, we provide baseline results for a random
forest as well as a convolutional neural network based approach.
Results show the complementary strengths and weaknesses of both
approaches and point out that the provided dataset represents an
open challenge for future research.