We present uLayout, a unified model for estimating room layout geometries from both perspective and panoramic images, whereas traditional solutions require different model designs for each image type. The key idea of our solution is to unify both domains into the equirectangular projection, particularly, allocating perspective images into the most suitable latitude coordinate to effectively exploit both domains seamlessly. To address the Field-of-View (FoV) difference between the input domains, we design uLayout with a shared feature extractor with an extra 1D-Convolution layer to condition each domain input differently. This conditioning allows us to efficiently formulate a column-wise feature regression problem regardless of the FoV input. This simple yet effective approach achieves competitive performance with current state-of-the-art solutions and shows for the first time a single end-to-end model for both domains. Extensive experiments in the real-world datasets, LSUN, Matterport3D, PanoContext, and Stanford 2D-3D evidence the contribution of our approach. Code is available atthis https URL.
View on arXiv@article{lee2025_2503.21562,
title={ uLayout: Unified Room Layout Estimation for Perspective and Panoramic Images },
author={ Jonathan Lee and Bolivar Solarte and Chin-Hsuan Wu and Jin-Cheng Jhang and Fu-En Wang and Yi-Hsuan Tsai and Min Sun },
journal={arXiv preprint arXiv:2503.21562},
year={ 2025 }
}