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The volume and diversity of training data are critical for modern deeplearningbased methods. Compared to the massive amount of labeled perspectiveimages, 360 panoramic images fall short in both volume and diversity. In thispaper, we propose PanoMixSwap, a novel data augmentation technique specificallydesigned for indoor panoramic images. PanoMixSwap explicitly mixes variousbackground styles, foreground furniture, and room layouts from the existingindoor panorama datasets and generates a diverse set of new panoramic images toenrich the datasets. We first decompose each panoramic image into itsconstituent parts: background style, foreground furniture, and room layout.Then, we generate an augmented image by mixing these three parts from threedifferent images, such as the foreground furniture from one image, thebackground style from another image, and the room structure from the thirdimage. Our method yields high diversity since there is a cubical increase inimage combinations. We also evaluate the effectiveness of PanoMixSwap on twoindoor scene understanding tasks: semantic segmentation and layout estimation.Our experiments demonstrate that state-of-the-art methods trained withPanoMixSwap outperform their original setting on both tasks consistently.

PanoMixSwap Panorama Mixing via Structural Swapping for Indoor Scene Understanding