# A Concept for Reconstructing Stucco Statues from historic Sketches using synthetic Data only

arXiv: https://arxiv.org/abs/2402.05593 · HTML: https://mlsanity.com/paper/2402.05593/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 23 references that are in the map.
- First posted on arXiv 8 Feb 2024.

## Builds on (most cited first, 12 of 12)

- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization](https://mlsanity.com/paper/1703.06868/) (2017)
- [Unsupervised Domain Adaptation by Backpropagation](https://mlsanity.com/paper/1409.7495/) (2014)
- [Alias-Free Generative Adversarial Networks](https://mlsanity.com/paper/2106.12423/) (2021)
- [High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs](https://mlsanity.com/paper/1711.11585/) (2017)
- [SDFDiff: Differentiable Rendering of Signed Distance Fields for 3D Shape Optimization](https://mlsanity.com/paper/1912.07109/) (2019)
- [Learning Efficient Point Cloud Generation for Dense 3D Object Reconstruction](https://mlsanity.com/paper/1706.07036/) (2017)
- [Learning to generate line drawings that convey geometry and semantics](https://mlsanity.com/paper/2203.12691/) (2022)
- [Pix2Vox++: Multi-scale Context-aware 3D Object Reconstruction from Single and Multiple Images](https://mlsanity.com/paper/2006.12250/) (2020)
- [3D Shape Reconstruction from Sketches via Multi-view Convolutional Networks](https://mlsanity.com/paper/1707.06375/) (2017)
- [DeceptionNet: Network-Driven Domain Randomization](https://mlsanity.com/paper/1904.02750/) (2019)
- [Style-transfer GANs for bridging the domain gap in synthetic pose estimator training](https://mlsanity.com/paper/2004.13681/) (2020)

## Built on by (most cited first, 1 of 1)

- [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)

In medieval times, stuccoworkers used a red color, called sinopia, to first create a sketch of the to-be-made statue on the wall. Today, many of these statues are destroyed, but using the original drawings, deriving from the red color also called sinopia, we can reconstruct how the final statue might have looked.We propose a fully-automated approach to reconstruct a point cloud and show preliminary results by generating a color-image, a depth-map, as well as surface normals requiring only a single sketch, and without requiring a collection of other, similar samples. Our proposed solution allows real-time reconstruction on-site, for instance, within an exhibition, or to generate a useful starting point for an expert, trying to manually reconstruct the statue, all while using only synthetic data for training.