# SliceHub: Augmenting Shared 3D Model Repositories with Slicing Results for 3D Printing

arXiv: https://arxiv.org/abs/2109.14722 · HTML: https://mlsanity.com/paper/2109.14722/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Sep 2021.

## Built on by (most cited first, 5 of 5)

- [Style2Fab: Functionality-Aware Segmentation for Fabricating Personalized 3D Models with Generative AI](https://mlsanity.com/paper/2309.06379/) (2023)
- [Shaping Realities: Enhancing 3D Generative AI with Fabrication Constraints](https://mlsanity.com/paper/2404.10142/) (2024)
- [TactStyle: Generating Tactile Textures with Generative AI for Digital Fabrication](https://mlsanity.com/paper/2503.02007/) (2025)
- [MechStyle: Augmenting Generative AI with Mechanical Simulation to Create Stylized and Structurally Viable 3D Models](https://mlsanity.com/paper/2509.20571/) (2025)
- [InstructMesh: Selective Refinement of Generative 3D Models for Fabrication](https://mlsanity.com/paper/2608.28534/) (2026)

## Abstract (arXiv)

In this paper, we explore how to augment shared 3D model repositories, such as Thingiverse, with slicing results that are readily available to all users. By having print time and material consumption for different print resolution profiles and model scales available in real-time, users are able to explore different slicing configurations efficiently to find the one that best fits their time and material constraints. To prototype this idea, we build a system called SliceHub, which consists of three components: (1) a repository with an evolving database of 3D models, for which we store the print time and material consumption for various print resolution profiles and model scales, (2) a user interface integrated into an existing slicer that allows users to explore the slicing information from the 3D models, and (3) a computational infrastructure to quickly generate new slicing results, either through parallel slicing of multiple print resolution profiles and model scales or through interpolation. We motivate our work with a formative study of the challenges faced by users of existing slicers and provide a technical evaluation of the SliceHub system.