# Hierarchical Detail Enhancing Mesh-Based Shape Generation with 3D Generative Adversarial Network

arXiv: https://arxiv.org/abs/1709.07581 · HTML: https://mlsanity.com/paper/1709.07581/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Sep 2017.

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

- [Shape As Points: A Differentiable Poisson Solver](https://mlsanity.com/paper/2106.03452/) (2021)
- [Locally Attentional SDF Diffusion for Controllable 3D Shape Generation](https://mlsanity.com/paper/2305.04461/) (2023)
- [SDF-StyleGAN: Implicit SDF-Based StyleGAN for 3D Shape Generation](https://mlsanity.com/paper/2206.12055/) (2022)
- [Multiview Regenerative Morphing with Dual Flows](https://mlsanity.com/paper/2208.01287/) (2022)
- [3D GANs and Latent Space: A comprehensive survey](https://mlsanity.com/paper/2304.03932/) (2023)
- [Point2Building: Reconstructing Buildings from Airborne LiDAR Point Clouds](https://mlsanity.com/paper/2403.02136/) (2024)

## Abstract (arXiv)

Automatic mesh-based shape generation is of great interest across a wide range of disciplines, from industrial design to gaming, computer graphics and various other forms of digital art. While most traditional methods focus on primitive based model generation, advances in deep learning made it possible to learn 3-dimensional geometric shape representations in an end-to-end manner. However, most current deep learning based frameworks focus on the representation and generation of voxel and point-cloud based shapes, making it not directly applicable to design and graphics communities. This study addresses the needs for automatic generation of mesh-based geometries, and propose a novel framework that utilizes signed distance function representation that generates detail preserving three-dimensional surface mesh by a deep learning based approach.