  We propose a self-supervised method for partial point set registration. While
recent proposed learning-based methods have achieved impressive registration
performance on the full shape observations, these methods mostly suffer from
performance degradation when dealing with partial shapes. To bridge the
performance gaps between partial point set registration with full point set
registration, we proposed to incorporate a shape completion network to benefit
the registration process. To achieve this, we design a latent code for each
pair of shapes, which can be regarded as a geometric encoding of the target
shape. By doing so, our model does need an explicit feature embedding network
to learn the feature encodings. More importantly, both our shape completion
network and the point set registration network take the shared latent codes as
input, which are optimized along with the parameters of two decoder networks in
the training process. Therefore, the point set registration process can thus
benefit from the joint optimization process of latent codes, which are enforced
to represent the information of full shape instead of partial ones. In the
inference stage, we fix the network parameter and optimize the latent codes to
get the optimal shape completion and registration results. Our proposed method
is pure unsupervised and does not need any ground truth supervision.
Experiments on the ModelNet40 dataset demonstrate the effectiveness of our
model for partial point set registration.
