# Probabilistic Registration for Gaussian Process 3D shape modelling in the presence of extensive missing data

arXiv: https://arxiv.org/abs/2203.14113 · HTML: https://mlsanity.com/paper/2203.14113/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 52 references that are in the map.
- First posted on arXiv 26 Mar 2022.

## Builds on (most cited first, 12 of 12)

- [Variational Inference: A Review for Statisticians](https://mlsanity.com/paper/1601.00670/) (2016)
- [Towards a complete 3D morphable model of the human head](https://mlsanity.com/paper/1911.08008/) (2019)
- [Combining 3D Morphable Models: A Large scale Face-and-Head Model](https://mlsanity.com/paper/1903.03785/) (2019)
- [Multilinear Wavelets: A Statistical Shape Space for Human Faces](https://mlsanity.com/paper/1401.2818/) (2014)
- [Gaussian Process Morphable Models](https://mlsanity.com/paper/1603.07254/) (2016)
- [LSG-CPD: Coherent Point Drift with Local Surface Geometry for Point Cloud Registration](https://mlsanity.com/paper/2103.15039/) (2021)
- [A Survey on Non-rigid 3D Shape Analysis](https://mlsanity.com/paper/1812.10111/) (2018)
- [Shape-aware Surface Reconstruction from Sparse 3D Point-Clouds](https://mlsanity.com/paper/1602.08425/) (2016)
- [Clustering of the Blendshape Facial Model](https://mlsanity.com/paper/2110.15313/) (2021)
- [From noisy point clouds to complete ear shapes: unsupervised pipeline](https://mlsanity.com/paper/2008.09831/) (2020)
- [Towards Fine-grained 3D Face Dense Registration: An Optimal Dividing and Diffusing Method](https://mlsanity.com/paper/2109.11204/) (2021)
- [Sphere Face Model:A 3D Morphable Model with Hypersphere Manifold Latent Space](https://mlsanity.com/paper/2112.02238/) (2021)

## Abstract (arXiv)

We propose a shape fitting/registration method based on a Gaussian Processes formulation, suitable for shapes with extensive regions of missing data. Gaussian Processes are a proven powerful tool, as they provide a unified setting for shape modelling and fitting. While the existing methods in this area prove to work well for the general case of the human head, when looking at more detailed and deformed data, with a high prevalence of missing data, such as the ears, the results are not satisfactory. In order to overcome this, we formulate the shape fitting problem as a multi-annotator Gaussian Process Regression and establish a parallel with the standard probabilistic registration. The achieved method SFGP shows better performance when dealing with extensive areas of missing data when compared to a state-of-the-art registration method and current approaches for registration with pre-existing shape models. Experiments are conducted both for a 2D small dataset with diverse transformations and a 3D dataset of ears.