PetroSurf3D - A high-resolution 3D Dataset of Rock Art for Surface Segmentation
PetroSurf3D - A high-resolution 3D Dataset of Rock Art for Surface Segmentation
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PetroSurf3D - 用于表面分割的高分辨率岩石艺术 3D 数据集
DOI：
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发表时间：
2016
期刊：
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通讯作者：
H. Bischof
中科院分区：
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作者：
Georg Poier;Markus Seidl;M. Zeppelzauer;Christian Reinbacher;M. Schaich;G. Bellandi;A. Marretta;H. Bischof
古代岩石雕刻（所谓的岩画）代表了描述我们祖先生活的最早幸存的文物之一。最近，现代3D扫描技术通过提供岩石表面的高分辨率重建而在岩石艺术文献领域中找到了应用。重建结果证明了新的3D技术的优势，并有可能取代传统的（手动）记录技术的考古学家。岩画的分割是岩画文献分析中的一项重要任务。为了促进这一繁琐步骤的自动化，我们提出了一个高分辨率的天然岩石表面的3D表面数据集，这些数据集展示了不同的岩画以及准确的专家地面实况注释。据我们所知，该数据集是第一个允许亚毫米级表面分割的公共3D表面数据集。我们使用最先进的方法进行实验，以生成数据集的基线，并验证数据的大小和可变性足以成功采用最近的数据饥渴卷积神经网络（CNN）。此外，我们的实验表明，所提供的几何信息是成功的自动分割的关键，并强烈优于基于颜色的分割。介绍的数据集代表了一个新的基准3D表面分割方法一般，旨在促进未来不同方法之间的可比性。
Ancient rock engravings (so called petroglyphs) represent one of the earliest surviving artifacts describing life of our ancestors. Recently, modern 3D scanning techniques found their application in the domain of rock art documentation by providing high-resolution reconstructions of rock surfaces. Reconstruction results demonstrate the strengths of novel 3D techniques and have the potential to replace the traditional (manual) documentation techniques of archaeologists. An important analysis task in rock art documentation is the segmentation of petroglyphs. To foster automation of this tedious step, we present a high-resolution 3D surface dataset of natural rock surfaces which exhibit different petroglyphs together with accurate expert ground-truth annotations. To our knowledge, this dataset is the first public 3D surface dataset which allows for surface segmentation at sub-millimeter scale. We conduct experiments with state-of-the-art methods to generate a baseline for the dataset and verify that the size and variability of the data is sufficient to successfully adopt even recent data-hungry Convolutional Neural Networks (CNNs). Furthermore, we experimentally demonstrate that the provided geometric information is key to successful automatic segmentation and strongly outperforms color-based segmentation. The introduced dataset represents a novel benchmark for 3D surface segmentation methods in general and is intended to foster comparability among different approaches in future.