Unsupervised Category-Specific Partial Point Set Registration via Joint Shape Completion and Registration
Li Xiang, Lingjing Wang, Yi Fang
Abstract
We propose a self-supervised method for partial point set registration. Although recently proposed learning-based methods demonstrate impressive registration performance on full shape observations, these methods often suffer from performance degradation when dealing with partial shapes. To bridge the performance gap between partial and full point set registration, we propose to incorporate a shape completion network to benefit the registration process. To achieve this, we introduce a learnable latent code for each pair of shapes, which can be regarded as the geometric encoding of the target shape. By doing so, our model does not require an explicit feature embedding network to learn the feature encodings. More importantly, both our shape completion and point set registration networks take the shared latent codes as input, which are optimized simultaneously with the parameters of two decoder networks in the training process. Therefore, the point set registration process can benefit from the joint optimization process of latent codes, which are enforced to represent the information of full shapes instead of partial ones. In the inference stage, we fix the network parameters and optimize the latent codes to obtain the optimal shape completion and registration results. Our proposed method is purely unsupervised and does not require ground truth supervision. Experiments on the ModelNet40 dataset demonstrate the effectiveness of our model for partial point set registration.