The development of powerful 3D scanning hardware and reconstruction
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 domain, where surface reconstructions are generated to
digitally preserve 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 and interactive segmentation of high-resolution
3D surface reconstructions from the archaeological domain. To foster research
in this field, we introduce a fully annotated and publicly available
large-scale 3D surface dataset including high-resolution meshes, depth maps and
point clouds as a novel benchmark dataset to the community. We provide baseline
results for our existing random forest-based approach and for the first time
investigate segmentation with convolutional neural networks (CNNs) on the data.
Results show that both approaches have complementary strengths and weaknesses
and that the provided dataset represents a challenge for future research.Comment: CBMI Submission; Dataset and more information can be found at
http://lrs.icg.tugraz.at/research/petroglyphsegmentation