# A Simple Approach to Intrinsic Correspondence Learning on Unstructured 3D Meshes

arXiv: https://arxiv.org/abs/1809.06664 · HTML: https://mlsanity.com/paper/1809.06664/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Sep 2018.

## Built on by (most cited first, 30 of 39)

- [MeshGPT: Generating Triangle Meshes with Decoder-Only Transformers](https://mlsanity.com/paper/2311.15475/) (2023)
- [MobRecon: Mobile-Friendly Hand Mesh Reconstruction from Monocular Image](https://mlsanity.com/paper/2112.02753/) (2021)
- [Subdivision-Based Mesh Convolution Networks](https://mlsanity.com/paper/2106.02285/) (2021)
- [Camera-Space Hand Mesh Recovery via Semantic Aggregation and Adaptive 2D-1D Registration](https://mlsanity.com/paper/2103.02845/) (2021)
- [Collaborative Learning for Hand and Object Reconstruction with Attention-guided Graph Convolution](https://mlsanity.com/paper/2204.13062/) (2022)
- [3D Pose Transfer with Correspondence Learning and Mesh Refinement](https://mlsanity.com/paper/2109.15025/) (2021)
- [Consistent 3D Hand Reconstruction in Video via self-supervised Learning](https://mlsanity.com/paper/2201.09548/) (2022)
- [A Simple Baseline for Efficient Hand Mesh Reconstruction](https://mlsanity.com/paper/2403.01813/) (2024)
- [Pre-training strategies and datasets for facial representation learning](https://mlsanity.com/paper/2103.16554/) (2021)
- [ScanTalk: 3D Talking Heads from Unregistered Scans](https://mlsanity.com/paper/2403.10942/) (2024)
- [Unsupervised 3D Pose Transfer with Cross Consistency and Dual Reconstruction](https://mlsanity.com/paper/2211.10278/) (2022)
- [Sparse to Dense Dynamic 3D Facial Expression Generation](https://mlsanity.com/paper/2105.07463/) (2021)
- [Learning Feature Aggregation for Deep 3D Morphable Models](https://mlsanity.com/paper/2105.02173/) (2021)
- [Identity-Aware Hand Mesh Estimation and Personalization from RGB Images](https://mlsanity.com/paper/2209.10840/) (2022)
- [FaceTuneGAN: Face Autoencoder for Convolutional Expression Transfer Using Neural Generative Adversarial Networks](https://mlsanity.com/paper/2112.00532/) (2021)
- [What's the Situation with Intelligent Mesh Generation: A Survey and Perspectives](https://mlsanity.com/paper/2211.06009/) (2022)
- [E(3)-Equivariant Mesh Neural Networks](https://mlsanity.com/paper/2402.04821/) (2024)
- [TransforMesh: A Transformer Network for Longitudinal modeling of Anatomical Meshes](https://mlsanity.com/paper/2109.00532/) (2021)
- [Deep Virtual Markers for Articulated 3D Shapes](https://mlsanity.com/paper/2108.09000/) (2021)
- [Generating Multiple 4D Expression Transitions by Learning Face Landmark Trajectories](https://mlsanity.com/paper/2208.00050/) (2022)
- [Generalizable Local Feature Pre-training for Deformable Shape Analysis](https://mlsanity.com/paper/2303.15104/) (2023)
- [Hybrid Neural Diffeomorphic Flow for Shape Representation and Generation via Triplane](https://mlsanity.com/paper/2307.01957/) (2023)
- [Locally Adaptive Neural 3D Morphable Models](https://mlsanity.com/paper/2401.02937/) (2024)
- [Beyond Fixed Topologies: Unregistered Training and Comprehensive Evaluation Metrics for 3D Talking Heads](https://mlsanity.com/paper/2410.11041/) (2024)
- [MASH: Masked Anchored SpHerical Distances for 3D Shape Representation and Generation](https://mlsanity.com/paper/2504.09149/) (2025)
- [Modeling Dynamics over Meshes with Gauge Equivariant Nonlinear Message Passing](https://mlsanity.com/paper/2310.19589/) (2023)
- [MAPConNet: Self-supervised 3D Pose Transfer with Mesh and Point Contrastive Learning](https://mlsanity.com/paper/2304.13819/) (2023)
- [SpATr: MoCap 3D Human Action Recognition based on Spiral Auto-encoder and Transformer Network](https://mlsanity.com/paper/2306.17574/) (2023)
- [SiMA-Hand: Boosting 3D Hand-Mesh Reconstruction by Single-to-Multi-View Adaptation](https://mlsanity.com/paper/2402.01389/) (2024)
- [Dual Mesh Convolutional Networks for Human Shape Correspondence](https://mlsanity.com/paper/2103.12459/) (2021)

## Abstract (arXiv)

The question of representation of 3D geometry is of vital importance when it comes to leveraging the recent advances in the field of machine learning for geometry processing tasks. For common unstructured surface meshes state-of-the-art methods rely on patch-based or mapping-based techniques that introduce resampling operations in order to encode neighborhood information in a structured and regular manner. We investigate whether such resampling can be avoided, and propose a simple and direct encoding approach. It does not only increase processing efficiency due to its simplicity - its direct nature also avoids any loss in data fidelity. To evaluate the proposed method, we perform a number of experiments in the challenging domain of intrinsic, non-rigid shape correspondence estimation. In comparisons to current methods we observe that our approach is able to achieve highly competitive results.