Robust Point-Cloud Registration based on Dense Point Matching and Probabilistic Modeling

  • Gustavo Marques Netto UFRGS
  • Manuel M. de Oliveira Neto UFRGS


We present techniques for 3D point-cloud registration that are 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 achieves superior results in general. For example, our approaches achieve registration error up to 45% smaller than these methods in partial point clouds for non-rigid registration, and up to 49% smaller on rigid registration.


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NETTO, Gustavo Marques; OLIVEIRA NETO, Manuel M. de. Robust Point-Cloud Registration based on Dense Point Matching and Probabilistic Modeling. In: WORKSHOP DE TESES E DISSERTAÇÕES - CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI), 36. , 2023, Rio Grande/RS. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2023 . p. 7-13. DOI: