  While 3D-3D registration is traditionally tacked by optimization-based
methods, recent work has shown that learning-based techniques could achieve
faster and more robust results. In this context, however, only PRNet can handle
the partial-to-partial registration scenario. Unfortunately, this is achieved
at the cost of relying on an iterative procedure, with a complex network
architecture. Here, we show that learning-based partial-to-partial registration
can be achieved in a one-shot manner, jointly reducing network complexity and
increasing registration accuracy. To this end, we propose an Optimal Transport
layer able to account for occluded points thanks to the use of outlier bins.
The resulting OPRNet framework outperforms the state of the art on standard
benchmarks, demonstrating better robustness and generalization ability than
existing techniques.
