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Detecting aligned 3D keypoints is essential under many scenarios such asobject tracking, shape retrieval and robotics. However, it is generally hard toprepare a high-quality dataset for all types of objects due to the ambiguity ofkeypoint itself. Meanwhile, current unsupervised detectors are unable togenerate aligned keypoints with good coverage. In this paper, we propose anunsupervised aligned keypoint detector, Skeleton Merger, which utilizesskeletons to reconstruct objects. It is based on an Autoencoder architecture.The encoder proposes keypoints and predicts activation strengths of edgesbetween keypoints. The decoder performs uniform sampling on the skeleton andrefines it into small point clouds with pointwise offsets. Then the activationstrengths are applied and the sub-clouds are merged. Composite Chamfer Distance(CCD) is proposed as a distance between the input point cloud and thereconstruction composed of sub-clouds masked by activation strengths. Wedemonstrate that Skeleton Merger is capable of detecting semantically-richsalient keypoints with good alignment, and shows comparable performance tosupervised methods on the KeypointNet dataset. It is also shown that thedetector is robust to noise and subsampling. Our code is available athttps://github.com/eliphatfs/SkeletonMerger.

Skeleton Merger: an Unsupervised Aligned Keypoint Detector