GPR-Net: Multi-view Layout Estimation via a Geometry-aware Panorama  Registration Network

Su, Jheng-Wei; Peng, Chi-Han; Wonka, Peter; Chu, Hung-Kuo

GPR-Net: Multi-view Layout Estimation via a Geometry-aware Panorama Registration Network

Files

2210.11419.pdf (6.15 MB)

Type

Preprint

Authors

Su, Jheng-Wei
Peng, Chi-Han
Wonka, Peter

Chu, Hung-Kuo

KAUST Department

Computer Science Program
Visual Computing Center (VCC)
Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

Date

2022-10-21

Abstract

Reconstructing 3D layouts from multiple 360◦ panoramas has received increasing attention recently as estimating a complete layout of a large-scale and complex room from a single panorama is very difficult. The state-of-the-art method, called PSMNet (Wang et al., 2022), introduces the first learning-based framework that jointly estimates the room layout and registration given a pair of panoramas. However, PSMNet relies on an approximate (i.e., ”noisy”) registration as input. Obtaining this input requires a solution for wide baseline registration which is a challenging problem. In this work, we present a complete multi-view panoramic layout estimation framework that jointly learns panorama registration and layout estimation given a pair of panoramas without relying on a pose prior. The major improvement over PSMNet comes from a novel Geometry-aware Panorama Registration Network or GPR-Net that effectively tackles the wide baseline registration problem by exploiting the layout geometry and computing fine-grained correspondences on the layout boundaries, instead of the global pixel-space. Our architecture consists of two parts. First, given two panoramas, we adopt a vision transformer to learn a set of 1D horizon features sampled on the panorama. These 1D horizon features encode the depths of individual layout boundary samples and the correspondence and covisibility maps between layout boundaries. We then exploit a non-linear registration module to convert these 1D horizon features into a set of corresponding 2D boundary points on the layout. Finally, we estimate the final relative camera pose via RANSAC and obtain the complete layout simply by taking the union of registered layouts. Experimental results indicate that our method achieves state-of-the-art performance in both panorama registration and layout estimation on a large-scale indoor panorama dataset ZInD (Cruz et al., 2021).