Robust point-cloud registration based on dense point matching and probabilistic modeling

Abstract

We present 3D point-cloud registration techniques suited for scenarios where robustness to outliers and missing regions is necessary, besides being applicable to both rigid and non-rigid configurations. Our techniques exploit advantages from deep learning models for dense point matching and from recent advances in probabilistic modeling of point-cloud registration. Such a combination produces context awareness and resilience to outliers and missing information. We demonstrate their effectiveness by comparing them to state-of-the-art methods and showing that ours achieve superior results on existing and proposed datasets.