Given a single RGB panorama, the goal of 3D layout reconstruction is to
estimate the room layout by predicting the corners, floor boundary, and ceiling
boundary. A common approach has been to use standard convolutional networks to
predict the corners and boundaries, followed by post-processing to generate the
3D layout. However, the space-varying distortions in panoramic images are not
compatible with the translational equivariance property of standard
convolutions, thus degrading performance. Instead, we propose to use spherical
convolutions. The resulting network, which we call OmniLayout performs
convolutions directly on the sphere surface, sampling according to inverse
equirectangular projection and hence invariant to equirectangular distortions.
Using a new evaluation metric, we show that our network reduces the error in
the heavily distorted regions (near the poles) by approx 25 % when compared to
standard convolutional networks. Experimental results show that OmniLayout
outperforms the state-of-the-art by approx 4% on two different benchmark
datasets (PanoContext and Stanford 2D-3D). Code is available at
this https URL

本文提出了使用球形卷积，设计了一个名为 OmniLayout 的网络，该网络直接在球面表面上进行卷积，并使用新的评估指标证明了该网络可以有效地解决全景图像中局部扭曲的问题，最终在两个基准数据集上的表现也优于其他同类网络。

OmniLayout: 室内球形全景图的房间布局重建

OmniLayout: Room Layout Reconstruction from Indoor Spherical Panoramas

We present a novel method to reconstruct the 3D layout of a room (walls,
floors, ceilings) from a single perspective view in challenging conditions, by
contrast with previous single-view methods restricted to cuboid-shaped layouts.
This input view can consist of a color image only, but considering a depth map
results in a more accurate reconstruction. Our approach is formalized as
solving a constrained discrete optimization problem to find the set of 3D
polygons that constitute the layout. In order to deal with occlusions between
components of the layout, which is a problem ignored by previous works, we
introduce an analysis-by-synthesis method to iteratively refine the 3D layout
estimate. As no dataset was available to evaluate our method quantitatively, we
created one together with several appropriate metrics. Our dataset consists of
293 images from ScanNet, which we annotated with precise 3D layouts. It offers
three times more samples than the popular NYUv2 303 benchmark, and a much
larger variety of layouts.

本文提出一种解决单视角条件下房间（墙壁，地板，天花板）的三维布局重建问题的新方法，使用离群点检测和优化方法，在处理布局中的遮挡时提高了重建的准确性，同时创建了扫描网数据集以便对方法进行数量化评价。