# SharinGAN: Combining Synthetic and Real Data for Unsupervised Geometry Estimation

arXiv: https://arxiv.org/abs/2006.04026 · HTML: https://mlsanity.com/paper/2006.04026/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jun 2020.

## Built on by (most cited first, 25 of 25)

- [Multi-Frame Self-Supervised Depth with Transformers](https://mlsanity.com/paper/2204.07616/) (2022)
- [Learning Optical Flow, Depth, and Scene Flow without Real-World Labels](https://mlsanity.com/paper/2203.15089/) (2022)
- [Human-Aided Saliency Maps Improve Generalization of Deep Learning](https://mlsanity.com/paper/2105.03492/) (2021)
- [Monocular Depth Estimation through Virtual-world Supervision and Real-world SfM Self-Supervision](https://mlsanity.com/paper/2103.12209/) (2021)
- [DualRefine: Self-Supervised Depth and Pose Estimation Through Iterative Epipolar Sampling and Refinement Toward Equilibrium](https://mlsanity.com/paper/2304.03560/) (2023)
- [PatchRefiner: Leveraging Synthetic Data for Real-Domain High-Resolution Monocular Metric Depth Estimation](https://mlsanity.com/paper/2406.06679/) (2024)
- [Self-Supervised Learning of Domain Invariant Features for Depth Estimation](https://mlsanity.com/paper/2106.02594/) (2021)
- [Learning Feature Decomposition for Domain Adaptive Monocular Depth Estimation](https://mlsanity.com/paper/2208.00160/) (2022)
- [Towards Scale Consistent Monocular Visual Odometry by Learning from the Virtual World](https://mlsanity.com/paper/2203.05712/) (2022)
- [Mind The Edge: Refining Depth Edges in Sparsely-Supervised Monocular Depth Estimation](https://mlsanity.com/paper/2212.05315/) (2022)
- [3D-PL: Domain Adaptive Depth Estimation with 3D-aware Pseudo-Labeling](https://mlsanity.com/paper/2209.09231/) (2022)
- [Task-guided Domain Gap Reduction for Monocular Depth Prediction in Endoscopy](https://mlsanity.com/paper/2310.01663/) (2023)
- [PatchRefiner V2: Fast and Lightweight Real-Domain High-Resolution Metric Depth Estimation](https://mlsanity.com/paper/2501.01121/) (2025)
- [H-GAN: the power of GANs in your Hands](https://mlsanity.com/paper/2103.15017/) (2021)
- [Co-training for Deep Object Detection: Comparing Single-modal and Multi-modal Approaches](https://mlsanity.com/paper/2104.11619/) (2021)
- [GenDepth: Generalizing Monocular Depth Estimation for Arbitrary Camera Parameters via Ground Plane Embedding](https://mlsanity.com/paper/2312.06021/) (2023)
- [On the Metrics for Evaluating Monocular Depth Estimation](https://mlsanity.com/paper/2302.10007/) (2023)
- [Depth-Relative Self Attention for Monocular Depth Estimation](https://mlsanity.com/paper/2304.12849/) (2023)
- [Consistency Regularisation for Unsupervised Domain Adaptation in Monocular Depth Estimation](https://mlsanity.com/paper/2405.17704/) (2024)
- [Unsupervised Monocular Depth Estimation Based on Hierarchical Feature-Guided Diffusion](https://mlsanity.com/paper/2406.09782/) (2024)
- [An Online Adaptation Method for Robust Depth Estimation and Visual Odometry in the Open World](https://mlsanity.com/paper/2504.11698/) (2025)
- [Improved Detection of Face Presentation Attacks Using Image Decomposition](https://mlsanity.com/paper/2103.12201/) (2021)
- [Focusing on Shadows for Predicting Heightmaps from Single Remotely Sensed RGB Images with Deep Learning](https://mlsanity.com/paper/2104.10874/) (2021)
- [Geometric Synthesis: A Free lunch for Large-scale Palmprint Recognition Model Pretraining](https://mlsanity.com/paper/2203.05703/) (2022)
- [MaskingDepth: Masked Consistency Regularization for Semi-supervised Monocular Depth Estimation](https://mlsanity.com/paper/2212.10806/) (2022)

## Abstract (arXiv)

We propose a novel method for combining synthetic and real images when training networks to determine geometric information from a single image. We suggest a method for mapping both image types into a single, shared domain. This is connected to a primary network for end-to-end training. Ideally, this results in images from two domains that present shared information to the primary network. Our experiments demonstrate significant improvements over the state-of-the-art in two important domains, surface normal estimation of human faces and monocular depth estimation for outdoor scenes, both in an unsupervised setting.