Mask-Homo: Pseudo Plane Mask-Guided Unsupervised Multi-Homography Estimation
Yasi Wang, Hong Liu, Chao Zhang, Lu Xu, Qiang Wang
Abstract
Homography estimation is a fundamental problem in computer vision. Previous works mainly focus on estimating either a single homography, or multiple homographies based on mesh grid division of the image. In practical scenarios, single homography is inadequate and often leads to a compromised result for multiple planes; while mesh grid multi-homography damages the plane distribution of the scene, and does not fully address the restriction to use homography. In this work, we propose a novel semantics guided multi-homography estimation framework, Mask-Homo, to provide an explicit solution to the multi-plane depth disparity problem. First, a pseudo plane mask generation module is designed to obtain multiple correlated regions that follow the plane distribution of the scene. Then, multiple local homography transformations, each of which aligns a correlated region precisely, are predicted and corresponding warped images are fused to obtain the final result. Furthermore, a new metric, Mask-PSNR, is proposed for more comprehensive evaluation of alignment. Extensive experiments are conducted to verify the effectiveness of the proposed method. Our code is available at https://github.com/SAITPublic/MaskHomo.
BibTeX
@article{Wang_Liu_Zhang_Xu_Wang_2024, title={Mask-Homo: Pseudo Plane Mask-Guided Unsupervised Multi-Homography Estimation}, volume={38}, url={https://ojs.aaai.org/index.php/AAAI/article/view/28379}, DOI={10.1609/aaai.v38i6.28379}, abstractNote={Homography estimation is a fundamental problem in computer vision. Previous works mainly focus on estimating either a single homography, or multiple homographies based on mesh grid division of the image. In practical scenarios, single homography is inadequate and often leads to a compromised result for multiple planes; while mesh grid multi-homography damages the plane distribution of the scene, and does not fully address the restriction to use homography. In this work, we propose a novel semantics guided multi-homography estimation framework, Mask-Homo, to provide an explicit solution to the multi-plane depth disparity problem. First, a pseudo plane mask generation module is designed to obtain multiple correlated regions that follow the plane distribution of the scene. Then, multiple local homography transformations, each of which aligns a correlated region precisely, are predicted and corresponding warped images are fused to obtain the final result. Furthermore, a new metric, Mask-PSNR, is proposed for more comprehensive evaluation of alignment. Extensive experiments are conducted to verify the effectiveness of the proposed method. Our code is available at https://github.com/SAITPublic/MaskHomo.}, number={6}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Wang, Yasi and Liu, Hong and Zhang, Chao and Xu, Lu and Wang, Qiang}, year={2024}, month={Mar.}, pages={5678-5685} }