We address the limitations of Deep learning models for 3D
geometry segmentation by using Conditional Random fields (CRF). We
show that CRFs can take advantage of the neighbouring structure of point
clouds to assist the learning of the Deep Learning models (DL). Our hybrid
PN-CRF model is able to learn more optimal weights by taking advantage
of equal-segmentation assignments to neighbouring points. As a result,
it increases the robustness in the model specially for segmentation tasks
where correctly detecting the boundaries between segmentations is very
important.
Original languageEnglish
Title of host publicationEuropean Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
PublisherCiaco
Number of pages6
Volume27
ISBN (Print)978-287-587-065-0
Publication statusPublished - 24 Apr 2019
EventEuropean Symposium on Artificial Neural Networks 2019 - Brugge, Belgium
Duration: 24 Apr 201926 Apr 2019

Conference

ConferenceEuropean Symposium on Artificial Neural Networks 2019
CountryBelgium
CityBrugge
Period24/04/1926/04/19

ID: 40506071