Monocular catadioptric panoramic depth estimation via caustics-based virtual scene transition

Existing catadioptric panoramic depth estimation systems usually require two panoramic imaging subsystems to achieve binocular disparity. The system structures are complicated and only sparse depth maps can be obtained. We present a novel monocular catadioptric panoramic depth estimation method that achieves dense depth maps of panoramic scenes using a single unmodified conventional catadioptric panoramic imaging system. Caustics model the reflection of the curved mirror and establish the distance relationship between the virtual and real panoramic scenes to overcome the nonlinear problem of the curved mirror. Virtual scene depth is then obtained by applying our structure classification regularization to depth from defocus. Finally, real panoramic scene depth is recovered using the distance relationship. Our method's effectiveness is demonstrated in experiments.