From noisy point clouds to complete ear shapes: unsupervised pipeline

TL;DR

This paper presents an unsupervised pipeline that processes noisy, incomplete 3D point clouds of ears to produce complete, corresponded datasets, improving 3D ear modeling despite data challenges.

Contribution

It introduces a novel unsupervised pipeline for processing noisy, occluded 3D ear scans and proposes a new registration method with superior performance.

Findings

The pipeline effectively completes missing data in noisy point clouds.

The proposed registration method outperforms existing state-of-the-art techniques.

The approach enables better 3D ear shape modeling from challenging data.

Abstract

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 methods and propose a new approach for one of the steps of the pipeline, with better performance for our data.