# Learning Manifold Patch-Based Representations of Man-Made Shapes

arXiv: https://arxiv.org/abs/1906.12337 · HTML: https://mlsanity.com/paper/1906.12337/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jun 2019.

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

- [Im2Vec: Synthesizing Vector Graphics without Vector Supervision](https://mlsanity.com/paper/2102.02798/) (2021)
- [Abstracting Sketches through Simple Primitives](https://mlsanity.com/paper/2207.13543/) (2022)
- [SimpModeling: Sketching Implicit Field to Guide Mesh Modeling for 3D Animalmorphic Head Design](https://mlsanity.com/paper/2108.02548/) (2021)
- [SketchGPT: Autoregressive Modeling for Sketch Generation and Recognition](https://mlsanity.com/paper/2405.03099/) (2024)
- [One-to-many Reconstruction of 3D Geometry of cultural Artifacts using a synthetically trained Generative Model](https://mlsanity.com/paper/2402.08310/) (2024)

## Abstract (arXiv)

Choosing the right representation for geometry is crucial for making 3D models compatible with existing applications. Focusing on piecewise-smooth man-made shapes, we propose a new representation that is usable in conventional CAD modeling pipelines and can also be learned by deep neural networks. We demonstrate its benefits by applying it to the task of sketch-based modeling. Given a raster image, our system infers a set of parametric surfaces that realize the input in 3D. To capture piecewise smooth geometry, we learn a special shape representation: a deformable parametric template composed of Coons patches. Naively training such a system, however, is hampered by non-manifold artifacts in the parametric shapes and by a lack of data. To address this, we introduce loss functions that bias the network to output non-self-intersecting shapes and implement them as part of a fully self-supervised system, automatically generating both shape templates and synthetic training data. We develop a testbed for sketch-based modeling, demonstrate shape interpolation, and provide comparison to related work.