We propose an algorithm to predict room layout from a single image that
generalizes across panoramas and perspective images, cuboid layouts and more
general layouts (e.g. L-shape room). Our method operates directly on the
panoramic image, rather than decomposing into perspective images as do recent
works. Our network architecture is similar to that of RoomNet, but we show
improvements due to aligning the image based on vanishing points, predicting
multiple layout elements (corners, boundaries, size and translation), and
fitting a constrained Manhattan layout to the resulting predictions. Our method
compares well in speed and accuracy to other existing work on panoramas,
achieves among the best accuracy for perspective images, and can handle both
cuboid-shaped and more general Manhattan layouts.

该研究提出了一种算法，可以从单张全景图像中预测房间布局，不仅适用于全景图像和透视图像，也适用于长方体布局和更一般的布局形式，方法采用基于消失点的图像对齐，预测多个布局元素，并将结果拟合为受限制的曼哈顿布局，速度和准确率都达到了现有全景图像相关算法的较好水平，并可以处理长方体形状和更一般的曼哈顿布局。