  We present the first self-supervised method to train panoramic room layout
estimation models without any labeled data. Unlike per-pixel dense depth that
provides abundant correspondence constraints, layout representation is sparse
and topological, hindering the use of self-supervised reprojection consistency
on images. To address this issue, we propose Differentiable Layout View
Rendering, which can warp a source image to the target camera pose given the
estimated layout from the target image. As each rendered pixel is
differentiable with respect to the estimated layout, we can now train the
layout estimation model by minimizing reprojection loss. Besides, we introduce
regularization losses to encourage Manhattan alignment, ceiling-floor
alignment, cycle consistency, and layout stretch consistency, which further
improve our predictions. Finally, we present the first self-supervised results
on ZilloIndoor and MatterportLayout datasets. Our approach also shows promising
solutions in data-scarce scenarios and active learning, which would have an
immediate value in the real estate virtual tour software. Code is available at
https://github.com/joshua049/Stereo-360-Layout.
