# Shape from Shading through Shape Evolution

arXiv: https://arxiv.org/abs/1712.02961 · HTML: https://mlsanity.com/paper/1712.02961/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Dec 2017.

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

- [State of the Art in Dense Monocular Non-Rigid 3D Reconstruction](https://mlsanity.com/paper/2210.15664/) (2022)
- [Multi-view Gradient Consistency for SVBRDF Estimation of Complex Scenes under Natural Illumination](https://mlsanity.com/paper/2202.13017/) (2022)
- [SimpleProc: Fully Procedural Synthetic Data from Simple Rules for Multi-View Stereo](https://mlsanity.com/paper/2604.04925/) (2026)
- [AutoSynth: Learning to Generate 3D Training Data for Object Point Cloud Registration](https://mlsanity.com/paper/2309.11170/) (2023)
- [High-frequency shape recovery from shading by CNN and domain adaptation](https://mlsanity.com/paper/2108.02937/) (2021)
- [Face Normal Estimation from Rags to Riches](https://mlsanity.com/paper/2601.01950/) (2026)

## Abstract (arXiv)

In this paper, we address the shape-from-shading problem by training deep networks with synthetic images. Unlike conventional approaches that combine deep learning and synthetic imagery, we propose an approach that does not need any external shape dataset to render synthetic images. Our approach consists of two synergistic processes: the evolution of complex shapes from simple primitives, and the training of a deep network for shape-from-shading. The evolution generates better shapes guided by the network training, while the training improves by using the evolved shapes. We show that our approach achieves state-of-the-art performance on a shape-from-shading benchmark.