DuLa-Net: A Dual-Projection Network for Estimating Room Layouts from a Single RGB Panorama

Understand

We present a deep learning framework, called DuLa-Net, to predict Manhattan-world 3D room layouts from a single RGB panorama.

• To achieve better prediction accuracy, our method leverages two projections of the panorama at once, namely the equirectangular panorama-view and the perspective ceiling-view, that each contains different clues about the room layouts.
• Our network architecture consists of two encoder-decoder branches for analyzing each of the two views.
• In addition, a novel feature fusion structure is proposed to connect the two branches, which are then jointly trained to predict the 2D floor plans and layout heights.