TY - GEN
T1 - The 3D-Pitoti Dataset: A Dataset for high-resolution 3D Surface Segmentation
AU - Poier, Georg
AU - Seidl, Markus
AU - Zeppelzauer, Matthias
AU - Reinbacher, Christian
AU - Schaich, Martin
AU - Bellandi, Giovanna
AU - Marretta, Alberto
AU - Bischof, Horst
PY - 2017
Y1 - 2017
N2 - 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 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.
AB - 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 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.
KW - Dataset
KW - Petroglyphs
KW - Segmentation
KW - 3D Surface Segmentation
U2 - 10.1145/3095713.3095719
DO - 10.1145/3095713.3095719
M3 - Conference paper
T3 - ACM International Conference Proceeding Series
BT - Proceedings of the 15th International Workshop on Content-Based Multimedia Indexing
PB - Association of Computing Machinery
T2 - 15th International Workshop on Content-Based Multimedia Indexing
Y2 - 19 June 2017 through 21 June 2017
ER -