@Misc{Netto:2023:RoPoRe,
author = "Netto, Gustavo Marques",
title = "Robust Point-Cloud Registration based on Dense Point Matching and
Probabilistic Modeling",
year = "2023",
date = "Nov. 06-09, 2023",
keywords = "Point-cloud registration, rigid registration, non-rigid
registration, dense point matching.",
targetfile = "autorizacao_publicacao_assinada.pdf",
abstract = "This thesis presents techniques for 3D point-cloud registration
that are robust to outliers and missing regions. They tackle
non-rigid and rigid registration and exploit the advantages of
deep learning for dense point matching. This is done by proposing
a single new neural network to solve both registration types. Our
network uses a recently proposed attention mechanism and
explicitly accounts for missing correspondences, which is key to
its performance. Additionally, we use recent advances in
probabilistic modeling to further refine the correspondences
created by our network during non-rigid registration. Such a
combination of deep learning and probabilistic modeling produces
context awareness and enforces motion coherence, which makes our
approach resilient to outliers and missing information. We
demonstrate the effectiveness of our techniques by comparing them
to state-of-the-art methods. Our comparisons use datasets
containing noise, partial point clouds, and irregular sampling.
The experiments show that our techniques obtain superior results
in general. For example, our approaches achieve a registration
error up to 45% smaller than other techniques in partial point
clouds for non-rigid registration, and up to 49% smaller on rigid
registration. We also discuss additional aspects of our techniques
such as robustness to different levels of noise and to different
numbers of samples in the point clouds. Finally, we tackle the
lack of datasets with ground truth for supervised training of
non-rigid registration models by presenting a self-supervised
strategy based on random deformations.",
affiliation = "UFRGS",
language = "en",
ibi = "8JMKD3MGPEW34M/49U8AAP",
url = "http://urlib.net/ibi/8JMKD3MGPEW34M/49U8AAP",
urlaccessdate = "2024, May 02"
}