Unsupervised Partial Point Set Registration via Joint Shape Completion and Registration

Understand

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.