# STN-Homography: estimate homography parameters directly

arXiv: https://arxiv.org/abs/1906.02539 · HTML: https://mlsanity.com/paper/1906.02539/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jun 2019.

## Built on by (most cited first, 2 of 2)

- [Towards Scalable Unpaired Virtual Try-On via Patch-Routed Spatially-Adaptive GAN](https://mlsanity.com/paper/2111.10544/) (2021)
- [PASTA-GAN++: A Versatile Framework for High-Resolution Unpaired Virtual Try-on](https://mlsanity.com/paper/2207.13475/) (2022)

## Abstract (arXiv)

In this paper, we introduce the STN-Homography model to directly estimate the homography matrix between image pair. Different most CNN-based homography estimation methods which use an alternative 4-point homography parameterization, we use prove that, after coordinate normalization, the variance of elements of coordinate normalized 3×3 homography matrix is very small and suitable to be regressed well with CNN. Based on proposed STN-Homography, we use a hierarchical architecture which stacks several STN-Homography models and successively reduce the estimation error. Effectiveness of the proposed method is shown through experiments on MSCOCO dataset, in which it significantly outperforms the state-of-the-art. The average processing time of our hierarchical STN-Homography with 1 stage is only 4.87 ms on the GPU, and the processing time for hierarchical STN-Homography with 3 stages is 17.85 ms. The code will soon be open sourced.