# Laplacian ICP for Progressive Registration of 3D Human Head Meshes

arXiv: https://arxiv.org/abs/2302.02194 · HTML: https://mlsanity.com/paper/2302.02194/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 25 references that are in the map.
- First posted on arXiv 4 Feb 2023.

## Builds on (most cited first, 8 of 8)

- [DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation](https://mlsanity.com/paper/1901.05103/) (2019)
- [Points2Surf: Learning Implicit Surfaces from Point Cloud Patches](https://mlsanity.com/paper/2007.10453/) (2020)
- [Towards a complete 3D morphable model of the human head](https://mlsanity.com/paper/1911.08008/) (2019)
- [Learning Implicit Functions for Topology-Varying Dense 3D Shape Correspondence](https://mlsanity.com/paper/2010.12320/) (2020)
- [LatentHuman: Shape-and-Pose Disentangled Latent Representation for Human Bodies](https://mlsanity.com/paper/2111.15113/) (2021)
- [Non-rigid 3D Shape Registration using an Adaptive Template](https://mlsanity.com/paper/1803.07973/) (2018)
- [Deep Functional Dictionaries: Learning Consistent Semantic Structures on 3D Models from Functions](https://mlsanity.com/paper/1805.09957/) (2018)
- [A Human Ear Reconstruction Autoencoder](https://mlsanity.com/paper/2010.03972/) (2020)

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

- [High-Fidelity Single-Image Head Modeling with Industry-Grade Topology](https://mlsanity.com/paper/2605.04524/) (2026)
- [Towards Geometric-Photometric Joint Alignment for Facial Mesh Registration](https://mlsanity.com/paper/2403.02629/) (2024)

## Abstract (arXiv)

We present a progressive 3D registration framework that is a highly-efficient variant of classical non-rigid Iterative Closest Points (N-ICP). Since it uses the Laplace-Beltrami operator for deformation regularisation, we view the overall process as Laplacian ICP (L-ICP). This exploits a `small deformation per iteration' assumption and is progressively coarse-to-fine, employing an increasingly flexible deformation model, an increasing number of correspondence sets, and increasingly sophisticated correspondence estimation. Correspondence matching is only permitted within predefined vertex subsets derived from domain-specific feature extractors. Additionally, we present a new benchmark and a pair of evaluation metrics for 3D non-rigid registration, based on annotation transfer. We use this to evaluate our framework on a publicly-available dataset of 3D human head scans (Headspace). The method is robust and only requires a small fraction of the computation time compared to the most popular classical approach, yet has comparable registration performance.