# DA Wand: Distortion-Aware Selection using Neural Mesh Parameterization

arXiv: https://arxiv.org/abs/2212.06344 · HTML: https://mlsanity.com/paper/2212.06344/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 82 references that are in the map.
- First posted on arXiv 13 Dec 2022.

## Builds on (most cited first, 11 of 11)

- [Thingi10K: A Dataset of 10,000 3D-Printing Models](https://mlsanity.com/paper/1605.04797/) (2016)
- [DiffusionNet: Discretization Agnostic Learning on Surfaces](https://mlsanity.com/paper/2012.00888/) (2020)
- [MeshCNN: A Network with an Edge](https://mlsanity.com/paper/1809.05910/) (2018)
- [MeshWalker: Deep Mesh Understanding by Random Walks](https://mlsanity.com/paper/2006.05353/) (2020)
- [The Vector Heat Method](https://mlsanity.com/paper/1805.09170/) (2018)
- [Deep Learning Based 3D Segmentation: A Survey](https://mlsanity.com/paper/2103.05423/) (2021)
- [Boundary First Flattening](https://mlsanity.com/paper/1704.06873/) (2017)
- [Autocomplete Textures for 3D Printing](https://mlsanity.com/paper/1703.05700/) (2017)
- [Discrete Geodesic Nets for Modeling Developable Surfaces](https://mlsanity.com/paper/1707.08360/) (2017)
- [Deep Patch-based Human Segmentation](https://mlsanity.com/paper/2007.05661/) (2020)
- [A Dataset and Benchmark for Mesh Parameterization](https://mlsanity.com/paper/2208.01772/) (2022)

## Built on by (most cited first, 9 of 9)

- [Flatten Anything: Unsupervised Neural Surface Parameterization](https://mlsanity.com/paper/2405.14633/) (2024)
- [PartUV: Part-Based UV Unwrapping of 3D Meshes](https://mlsanity.com/paper/2511.16659/) (2025)
- [FlexPara: Flexible Neural Surface Parameterization](https://mlsanity.com/paper/2504.19210/) (2025)
- [MeshTailor: Cutting Seams via Generative Mesh Traversal](https://mlsanity.com/paper/2603.27309/) (2026)
- [GeoScaler: Geometry and Rendering-Aware Downsampling of 3D Mesh Textures](https://mlsanity.com/paper/2311.16581/) (2023)
- [ParaPoint: Learning Global Free-Boundary Surface Parameterization of 3D Point Clouds](https://mlsanity.com/paper/2403.10349/) (2024)
- [Advances in Neural 3D Mesh Texturing: A Survey](https://mlsanity.com/paper/2606.00137/) (2026)
- [SeamGen: Artist-Aligned UV Seam Generation via Graph Flow Matching](https://mlsanity.com/paper/2607.12379/) (2026)
- [SeamFlow: Structure-Aware Flow Matching on Edge Probabilities for Artist-Like UV Unwrapping](https://mlsanity.com/paper/2609.04751/) (2026)

## Abstract (arXiv)

We present a neural technique for learning to select a local sub-region around a point which can be used for mesh parameterization. The motivation for our framework is driven by interactive workflows used for decaling, texturing, or painting on surfaces. Our key idea is to incorporate segmentation probabilities as weights of a classical parameterization method, implemented as a novel differentiable parameterization layer within a neural network framework. We train a segmentation network to select 3D regions that are parameterized into 2D and penalized by the resulting distortion, giving rise to segmentations which are distortion-aware. Following training, a user can use our system to interactively select a point on the mesh and obtain a large, meaningful region around the selection which induces a low-distortion parameterization. Our code and project page are currently available.