Automation of “ground truth” annotation for multi-view RGB-D object instance recognition datasets
Automation of “ground truth” annotation for multi-view RGB-D object instance recognition datasets
复制标题
多视图 RGB-D 对象实例识别数据集的“地面实况”注释自动化
DOI：
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
M. Vincze
中科院分区：
文献类型：
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作者：
A. Aldoma;Thomas Faulhammer;M. Vincze
为了减少与对象实例识别数据集的地面实况注释的获取相关的劳动强度，本文讨论了一种新的多视图识别方法，以自动化多视图RGB-D数据集中的单个图像的注释（对象实例和相关姿态）。与最近的单视图对象识别技术相结合，由多个Vantage点提供的补充信息导致以3D重建场景以及其中的对象假设的形式的环境的丰富且综合的表示。我们认为，这样的表示有利于提高识别的程度，恢复的结果，通过一个合适的3D假设验证阶段，非常类似的地面真相的场景中考虑。在两个大型数据集上，总共超过3500个对象实例，我们的方法产生了99.1%和93.2%的正确自动注释。这些结果证实了我们完成手头任务的方法。
Aiming at reducing the labour intensity associated with the acquisition of ground truth annotations for object instance recognition datasets, this paper discusses a novel multi-view recognition method to automate the annotation (object instances and associated poses) of individual images in multi-view RGB-D datasets. In combination with recent single-view object recognition techniques, the supplementary information provided by multiple vantage points results in a rich and integrated representation of the environment, in the form of a 3D reconstructed scene as well as object hypotheses therein. We argue that such a representation facilitates improved recognition to an extent that the recovered results, obtained by means of a suitable 3D hypotheses verification stage, closely resemble the ground truth of the scene under consideration. On two large datasets, totalling more than 3500 object instances, our method yields 99.1% and 93.2% correct automatic annotations. These results corroborate our approach for the task at hand.