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Providing omnidirectional depth along with RGB information is important fornumerous applications, eg, VR/AR. However, as omnidirectional RGB-D data is notalways available, synthesizing RGB-D panorama data from limited information ofa scene can be useful. Therefore, some prior works tried to synthesize RGBpanorama images from perspective RGB images; however, they suffer from limitedimage quality and can not be directly extended for RGB-D panorama synthesis. Inthis paper, we study a new problem: RGB-D panorama synthesis under thearbitrary configurations of cameras and depth sensors. Accordingly, we proposea novel bi-modal (RGB-D) panorama synthesis (BIPS) framework. Especially, wefocus on indoor environments where the RGB-D panorama can provide a complete 3Dmodel for many applications. We design a generator that fuses the bi-modalinformation and train it with residual-aided adversarial learning (RDAL). RDALallows to synthesize realistic indoor layout structures and interiors byjointly inferring RGB panorama, layout depth, and residual depth. In addition,as there is no tailored evaluation metric for RGB-D panorama synthesis, wepropose a novel metric to effectively evaluate its perceptual quality.Extensive experiments show that our method synthesizes high-quality indoorRGB-D panoramas and provides realistic 3D indoor models than prior methods.Code will be released upon acceptance.

BIPS: Bi-modal Indoor Panorama Synthesis via Residual Depth-aided Adversarial Learning