From Noisy Point Clouds to Complete Ear Shapes: Unsupervised Pipeline

Funding Information: This work was supported in part by the European Union’s Horizon 2020 Research And Innovation Programme through the Marie Skłodowska-Curie Project BIGMATH, under Agreement 812912, and in part by the Eureka Eurostars under Project E!11439 FacePrint. The work of Cláudia Soares was supported in part by the Strategic Project NOVA LINCS under Grant UIDB/04516/2020. Funding Information: This work was supported in part by the European Union's Horizon 2020 Research And Innovation Programme through the Marie Skiodowska-Curie Project BIGMATH, under Agreement 812912, and in part by the Eureka Eurostars under Project E11439 FacePrint. The work of Claudia Soares was supported in part by the Strategic Project NOVA LINCS under Grant UIDB/04516/2020. Publisher Copyright: © 2013 IEEE.Ears are a particularly difficult region of the human face to model, not only due to the non-rigid deformations existing between shapes but also to the challenges in processing the retrieved data. The first step towards obtaining a good model is to have complete scans in correspondence, but these usually present a higher amount of occlusions, noise and outliers when compared to most face regions, thus requiring a specific procedure. Therefore, we propose a complete pipeline taking as input unordered 3D point clouds with the aforementioned problems, and producing as output a dataset in correspondence, with completion of the missing data. We provide a comparison of several state-of-the-art registration and shape completion methods, concluding on the best choice for each of the steps.publishersversionpublishe