Abstract:Point cloud matching is an important procedure in a variety of computer vision tasks. Traditional point cloud matching methods have made great progress, while neural network-based approaches are becoming a trend, powered by their strong capabilities of feature extraction. Existing point matching neural networks, however, mainly focus on the rigid transformation. More complex transformations should also be considered in many scenarios. In this regard, the authors extend the rigid registration to non-rigid cases… Show more
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