In this work we present a method to train a plane-aware convolutional neural
network for dense depth and surface normal estimation as well as plane
boundaries from a single indoor $360^\circ$ image. Using our proposed loss
function, our network outperforms existing methods for single-view, indoor,
omnidirectional depth estimation and provides an initial benchmark for surface
normal prediction from $360^\circ$ images. Our improvements are due to the use
of a novel plane-aware loss that leverages principal curvature as an indicator
of planar boundaries. We also show that including geodesic coordinate maps as
network priors provides a significant boost in surface normal prediction
accuracy. Finally, we demonstrate how we can combine our network's outputs to
generate high quality 3D "pop-up" models of indoor scenes.

本研究提出了一种针对单个室内 360 度图像的平面感知卷积神经网络方法，可实现密集的深度和表面法向估计以及平面边界识别，此外，添加测地线坐标图作为网络先验可显著提高表面法向预测准确性，最终可以将网络的输出组合用于生成高质量的室内场景 3D 模型。