The problem of 3D layout recovery in indoor scenes has been a core research topic for over a decade. However, there are still several major challenges that remain unsolved. Among the most relevant ones, a major part of the state-of-the-art methods make implicit or explicit assumptions on the scenes -- e.g. box-shaped or Manhattan layouts. Also, current methods are computationally expensive and not suitable for real-time applications like robot navigation and AR/VR. In this work we present CFL (Corners for Layout), the first end-to-end model for 3D layout recovery on 360 images. Our experimental results show that we outperform the state of the art relaxing assumptions about the scene and at a lower cost. We also show that our model generalizes better to camera position variations than conventional approaches by using EquiConvs, a type of convolution applied directly on the sphere projection and hence invariant to the equirectangular distortions.

本文提出了一种基于360度图片的端到端模型CFL（Corners for Layout），用于恢复室内场景的三维布局，并通过使用EquiConvs（一种直接应用于球体投影的卷积），放宽场景假设和降低成本，优于目前的现有方法，并且对相机位置变化具有良好的泛化性能。

基于端到端的360度图像布局恢复的角点检测