CUPS is a novel Scene-Centric Unsupervised Panoptic Segmentation method that utilizes motion and depth from stereo pairs to create high-resolution pseudo-labels for training a monocular panoptic network. This approach allows for the effective segmentation of complex scenes without the need for annotated data, achieving superior performance compared to existing unsupervised methods, particularly on benchmarks like Cityscapes. CUPS demonstrates strong generalization capabilities across multiple datasets while significantly enhancing panoptic quality metrics.