Detecting aligned 3D keypoints is essential under many scenarios such as
object tracking, shape retrieval and robotics. However, it is generally hard to
prepare a high-quality dataset for all types of objects due to the ambiguity of
keypoint itself. Meanwhile, current unsupervised detectors are unable to
generate aligned keypoints with good coverage. In this paper, we propose an
unsupervised aligned keypoint detector, Skeleton Merger, which utilizes
skeletons to reconstruct objects. It is based on an Autoencoder architecture.
The encoder proposes keypoints and predicts activation strengths of edges
between keypoints. The decoder performs uniform sampling on the skeleton and
refines it into small point clouds with pointwise offsets. Then the activation
strengths are applied and the sub-clouds are merged. Composite Chamfer Distance
(CCD) is proposed as a distance between the input point cloud and the
reconstruction composed of sub-clouds masked by activation strengths. We
demonstrate that Skeleton Merger is capable of detecting semantically-rich
salient keypoints with good alignment, and shows comparable performance to
supervised methods on the KeypointNet dataset. It is also shown that the
detector is robust to noise and subsampling. Our code is available at
this https URL

本文提出了一种基于无监督学习的关节点检测算法，名为 Skeleton Merger，通过 Autoencoder 架构来提取关节点并预测点间的激活强度，进而利用骨架重建对象。该算法通过组合式 Chamfer 距离实现了对输入点云的复原，并发现它能够检测到具有良好对齐性的语义丰富的显著关节点，并在 KeypointNet 数据集上表现良好。