PanelNet: Understanding 360 Indoor Environment via Panel Representation
Haozheng Yu Lu He Bing Jian Weiwei Feng Shan Liu
Indoor 360 panoramas have two essential properties. (1) The panoramas are
continuous and seamless in the horizontal direction. (2) Gravity plays an
important role in indoor environment design. By leveraging these properties, we
present PanelNet, a framework that understands indoor environments using a
novel panel representation of 360 images. We represent an equirectangular
projection (ERP) as consecutive vertical panels with corresponding 3D panel
geometry. To reduce the negative impact of panoramic distortion, we incorporate
a panel geometry embedding network that encodes both the local and global
geometric features of a panel. To capture the geometric context in room design,
we introduce Local2Global Transformer, which aggregates local information
within a panel and panel-wise global context. It greatly improves the model
performance with low training overhead. Our method outperforms existing methods
on indoor 360 depth estimation and shows competitive results against
state-of-the-art approaches on the task of indoor layout estimation and
semantic segmentation.