Reviews: PRNet: Self-Supervised Learning for Partial-to-Partial Registration

Originality: this work tackles a traditional problem and achieves good performance improvement compared with previous states. The overall framework is quite novel. Unlike previous learning approaches which usually use a one-shot formula, the network is designed to be iterative, which is quite novel. In addition, there are also quite a few novel designs within the network. The most interesting one is the use of Gumbel-Softmax sampler within an actor-critic framework for sharp correspondence estimation.