# The Distance Transform and its Computation

arXiv: https://arxiv.org/abs/2106.03503 · HTML: https://mlsanity.com/paper/2106.03503/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 10 references that are in the map.
- First posted on arXiv 7 Jun 2021.

## Built on by (most cited first, 7 of 7)

- [HeadCraft: Modeling High-Detail Shape Variations for Animated 3DMMs](https://mlsanity.com/paper/2312.14140/) (2023)
- [Topology-Preserving Downsampling of Binary Images](https://mlsanity.com/paper/2407.17786/) (2024)
- [Learning Ordinality in Semantic Segmentation](https://mlsanity.com/paper/2407.20959/) (2024)
- [Scalar Field Prediction on Meshes Using Interpolated Multi-Resolution Convolutional Neural Networks](https://mlsanity.com/paper/2410.05522/) (2024)
- [Improved monocular depth prediction using distance transform over pre-semantic contours with self-supervised neural networks](https://mlsanity.com/paper/2605.08320/) (2026)
- [Real-Time Hand Gesture Identification in Thermal Images](https://mlsanity.com/paper/2303.02321/) (2023)
- [Towards Packaging Unit Detection for Automated Palletizing Tasks](https://mlsanity.com/paper/2308.06306/) (2023)

## Abstract (arXiv)

Distance transformation is an image processing technique used for many different applications. Related to a binary image, the general idea is to determine the distance of all background points to the nearest object point (or vice versa). In this tutorial, different approaches are explained in detail and compared using examples. Corresponding source code is provided to facilitate own investigations. A particular objective of this tutorial is to clarify the difference between arbitrary distance transforms and exact Euclidean distance transformations.