# Deep Active Surface Models

arXiv: https://arxiv.org/abs/2011.08826 · HTML: https://mlsanity.com/paper/2011.08826/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Nov 2020.

## Built on by (most cited first, 6 of 6)

- [What's the Situation with Intelligent Mesh Generation: A Survey and Perspectives](https://mlsanity.com/paper/2211.06009/) (2022)
- [Weakly Supervised Volumetric Image Segmentation with Deformed Templates](https://mlsanity.com/paper/2106.03987/) (2021)
- [AI-Powered Automated Model Construction for Patient-Specific CFD Simulations of Aortic Flows](https://mlsanity.com/paper/2503.12515/) (2025)
- [Deep Active Latent Surfaces for Medical Geometries](https://mlsanity.com/paper/2206.10241/) (2022)
- [Single-view 3D reconstruction via inverse procedural modeling](https://mlsanity.com/paper/2310.13373/) (2023)
- [NUDF: Neural Unsigned Distance Fields for high resolution 3D medical image segmentation](https://mlsanity.com/paper/2504.18344/) (2025)

## Abstract (arXiv)

Active Surface Models have a long history of being useful to model complex 3D surfaces but only Active Contours have been used in conjunction with deep networks, and then only to produce the data term as well as meta-parameter maps controlling them. In this paper, we advocate a much tighter integration. We introduce layers that implement them that can be integrated seamlessly into Graph Convolutional Networks to enforce sophisticated smoothness priors at an acceptable computational cost. We will show that the resulting Deep Active Surface Models outperform equivalent architectures that use traditional regularization loss terms to impose smoothness priors for 3D surface reconstruction from 2D images and for 3D volume segmentation.