3D- pitoti数据集:用于高分辨率3D表面分割的数据集

Georg Poier, Markus Seidl, M. Zeppelzauer, Christian Reinbacher, M. Schaich, G. Bellandi, A. Marretta, H. Bischof
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引用次数: 7

摘要

强大的三维扫描硬件和重建算法的发展有力地促进了不同领域三维表面重建的生成。这种3D重建的一个特别感兴趣的领域是文化遗产领域,在那里生成表面重建以数字方式保存历史文物。虽然目前在许多情况下重建的质量是足够的,但重建的三维数据的鲁棒分析(如分割、匹配和分类)仍然是一个开放的话题。本文针对高分辨率岩画三维表面重建的自动分割问题进行了研究。为了促进这一领域的研究,我们引入了一个完全注释的大规模3D表面数据集,包括高分辨率网格,深度图和点云,作为一个新的基准数据集,我们公开提供。此外,我们为随机森林和基于卷积神经网络的方法提供了基线结果。结果显示了两种方法的互补优势和弱点,并指出所提供的数据集代表了未来研究的公开挑战。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The 3D-Pitoti Dataset: A Dataset for high-resolution 3D Surface Segmentation
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.
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