论文标题
KeyPointNet：一个大规模的3D关键点数据集，该数据集汇总了许多人类注释
KeypointNet: A Large-scale 3D Keypoint Dataset Aggregated from Numerous Human Annotations
论文作者
论文摘要
检测3D对象关键点是图形和计算机视觉领域的极大兴趣。有几个2D和3D关键点数据集，目的是以数据驱动的方式解决此问题。但是，这些数据集要么缺乏可扩展性，要么对关键点的定义产生歧义。因此，我们介绍了KeyPointNet：通过利用大量人类注释，其中包含来自16个对象类别的第一个大规模和多样的3D关键数据集，其中包含103,450个关键点和8,234 3D模型。为了处理来自不同人的注释之间的不一致性，我们提出了一种新颖的方法，通过最小化忠诚度损失来自动汇总这些关键。最后，在我们提出的数据集中对十种最先进的方法进行了测试。我们的代码和数据可在https://github.com/qq456cvb/keypointnet上找到。
Detecting 3D objects keypoints is of great interest to the areas of both graphics and computer vision. There have been several 2D and 3D keypoint datasets aiming to address this problem in a data-driven way. These datasets, however, either lack scalability or bring ambiguity to the definition of keypoints. Therefore, we present KeypointNet: the first large-scale and diverse 3D keypoint dataset that contains 103,450 keypoints and 8,234 3D models from 16 object categories, by leveraging numerous human annotations. To handle the inconsistency between annotations from different people, we propose a novel method to aggregate these keypoints automatically, through minimization of a fidelity loss. Finally, ten state-of-the-art methods are benchmarked on our proposed dataset. Our code and data are available on https://github.com/qq456cvb/KeypointNet.
