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Panoramic image enables deeper understanding and more holistic perception of$360^\circ$ surrounding environment, which can naturally encode enriched scenecontext information compared to standard perspective image. Previous work hasmade lots of effort to solve the scene understanding task in a bottom-up form,thus each sub-task is processed separately and few correlations are explored inthis procedure. In this paper, we propose a novel method using depth prior forholistic indoor scene understanding which recovers the objects' shapes,oriented bounding boxes and the 3D room layout simultaneously from a singlepanorama. In order to fully utilize the rich context information, we design atransformer-based context module to predict the representation and relationshipamong each component of the scene. In addition, we introduce a real-worlddataset for scene understanding, including photo-realistic panoramas,high-fidelity depth images, accurately annotated room layouts, and orientedobject bounding boxes and shapes. Experiments on the synthetic and real-worlddatasets demonstrate that our method outperforms previous panoramic sceneunderstanding methods in terms of both layout estimation and 3D objectdetection.

PanoContext-Former: Panoramic Total Scene Understanding with a Transformer