# From Elevation Maps To Contour Lines: SVM and Decision Trees to Detect Violin Width Reduction

arXiv: https://arxiv.org/abs/2604.02446 · HTML: https://mlsanity.com/paper/2604.02446/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 7 references that are in the map.
- First posted on arXiv 2 Apr 2026.

## Builds on (most cited first, 5 of 5)

- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [Deep Sets](https://mlsanity.com/paper/1703.06114/) (2017)
- [Validation of a photogrammetric approach for the objective study of ancient bowed instruments](https://mlsanity.com/paper/2205.08745/) (2022)
- [A discussion about violin reduction: geometric analysis of contour lines and channel of minima](https://mlsanity.com/paper/2404.01995/) (2024)
- [Identification of Violin Reduction via Contour Lines Classification](https://mlsanity.com/paper/2507.07743/) (2025)

## Abstract (arXiv)

We explore the automatic detection of violin width reduction using 3D photogrammetric meshes. We compare SVM and Decision Trees applied to a geometry-based raw representation built from elevation maps with a more targeted, feature-engineered approach relying on parametric contour lines fitting. Although elevation maps occasionally achieve strong results, their performance does not surpass that of the contour-based inputs.