# From noisy point clouds to complete ear shapes: unsupervised pipeline

arXiv: https://arxiv.org/abs/2008.09831 · HTML: https://mlsanity.com/paper/2008.09831/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Aug 2020.

## Built on by (most cited first, 2 of 2)

- [Probabilistic Registration for Gaussian Process 3D shape modelling in the presence of extensive missing data](https://mlsanity.com/paper/2203.14113/) (2022)
- [Domain Elastic Transform: Bayesian Function Registration for High-Dimensional Scientific Data](https://mlsanity.com/paper/2603.21235/) (2026)

## Abstract (arXiv)

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.