Learning non-rigid registration in an end-to-end manner is challenging due to the inherent high degrees of freedom and the lack of labeled training data. In this paper, we resolve these two challenges simultaneously. First, we propose to represent the non-rigid transformation with a point-wise combination of several rigid transformations. This representation not only makes the solution space well-constrained but also enables our method to be solved iteratively with a recurrent framework, which greatly reduces the difficulty of learning. Second, we introduce a differentiable loss function that measures the 3D shape similarity on the projected multi-view 2D depth images so that our full framework can be trained end-to-end without ground truth supervision. Extensive experiments on several different datasets demonstrate that our proposed method outperforms the previous state-of-the-art by a large margin.

中文翻译：

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用于无监督曲面配准的循环多视图对齐网络

由于固有的高度自由度和缺少标记的训练数据，以端对端的方式学习非刚性注册非常具有挑战性。在本文中，我们同时解决了这两个挑战。首先，我们建议用几个刚性转换的逐点组合来表示非刚性转换。这种表示方式不仅使解决方案空间受到了很好的约束，而且使我们的方法可以使用递归框架进行迭代求解，从而大大降低了学习难度。其次，我们引入了可微分的损失函数，该函数可测量投影的多视图2D深度图像上的3D形状相似度，以便我们的完整框架可以在没有地面真理监督的情况下进行端到端训练。