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It has been shown that global scene understanding tasks like layoutestimation can benefit from wider field of views, and specifically sphericalpanoramas. While much progress has been made recently, all previous approachesrely on intermediate representations and postprocessing to produceManhattan-aligned estimates. In this work we show how to estimate full roomlayouts in a single-shot, eliminating the need for postprocessing. Our work isthe first to directly infer Manhattan-aligned outputs. To achieve this, ourdata-driven model exploits direct coordinate regression and is supervisedend-to-end. As a result, we can explicitly add quasi-Manhattan constraints,which set the necessary conditions for a homography-based Manhattan alignmentmodule. Finally, we introduce the geodesic heatmaps and loss and aboundary-aware center of mass calculation that facilitate higher qualitykeypoint estimation in the spherical domain. Our models and code are publiclyavailable at https://vcl3d.github.io/SingleShotCuboids/.

Single-Shot Cuboids: Geodesics-based End-to-end Manhattan Aligned Layout Estimation from Spherical Panoramas