Final published version
Licence: CC BY: Creative Commons Attribution 4.0 International License
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - Automatic Extraction and Labelling of Memorial Objects From 3D Point Clouds
AU - Arnold, Nicholas
AU - Angelov, Plamen
AU - Viney, Tim
AU - Atkinson, Peter
PY - 2021/4/23
Y1 - 2021/4/23
N2 - This research addresses the problem of automatic extraction of memorial objects from cultural heritage sites represented as scenes of 3D point clouds. Point clouds provide a fine spatial resolution and accurate proxy of the real world. However, how to use them directly is not always obvious. This is especially true for applications where extensive training data or computational resources are not available. In this paper, we present a methodology for automatic segmentation and labelling of cultural heritage objects from 3D point cloud scenes. The proposed methodology is based on machine learning techniques and, in particular, makes use of the concept of transfer learning. Memorial objects are segmented from the scene based on their geometric shape characteristic through a conditional multi-scale partitioning scheme. Then, high-level latent feature descriptors are extracted by a convolutional neural network pre-trained on different 3D object models from a standard dataset (e.g., ModelNet). Based on these descriptors, a classification model (multilayer perceptron) is trained and applied to obtain semantic labels. Experiments demonstrated that the proposed methodology is effective for the extraction and labelling of grave marker objects from cultural heritage sites.
AB - This research addresses the problem of automatic extraction of memorial objects from cultural heritage sites represented as scenes of 3D point clouds. Point clouds provide a fine spatial resolution and accurate proxy of the real world. However, how to use them directly is not always obvious. This is especially true for applications where extensive training data or computational resources are not available. In this paper, we present a methodology for automatic segmentation and labelling of cultural heritage objects from 3D point cloud scenes. The proposed methodology is based on machine learning techniques and, in particular, makes use of the concept of transfer learning. Memorial objects are segmented from the scene based on their geometric shape characteristic through a conditional multi-scale partitioning scheme. Then, high-level latent feature descriptors are extracted by a convolutional neural network pre-trained on different 3D object models from a standard dataset (e.g., ModelNet). Based on these descriptors, a classification model (multilayer perceptron) is trained and applied to obtain semantic labels. Experiments demonstrated that the proposed methodology is effective for the extraction and labelling of grave marker objects from cultural heritage sites.
KW - 3D
KW - Point Cloud
KW - Transfer Learning
KW - Cultural Heritage Management
KW - Object Extraction
U2 - 10.5334/jcaa.66
DO - 10.5334/jcaa.66
M3 - Journal article
VL - 4
SP - 79
EP - 93
JO - Journal of Computer Applications in Archaeology
JF - Journal of Computer Applications in Archaeology
IS - 1
ER -