Learning 3D-3D Correspondences for One-shot Partial-to-partial Registration

Understand

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.