Panoptic Segmentation for Indoor Environments using MaskDINO: An Experiment on the Impact of Contrast

Robot perception involves recognizing the surrounding environment, particularly in indoor spaces like kitchens, classrooms, and dining areas. This recognition is crucial for tasks such as object identification. Objects in indoor environments can be categorized into "things," with fixed and countable shapes (e.g., tables, chairs), and "stuff," which lack a fixed shape and cannot be counted (e.g., sky, walls). Object detection and instance segmentation methods excel in identifying "things," with instance segmentation providing more detailed representations than object detection. However, semantic segmentation can identify both "things" and "stuff" but lacks segmentation at the object level. Panoptic segmentation, a fusion of both methods, offers comprehensive object and stuff identification and object-level segmentation. Considerations need to be made regarding the variabilities of room conditions in contrast to implementing panoptic segmentation indoors. High or low contrast in the room potentially reduces the clarity of the shape of an object, thus affecting the segmentation results of that object. We experimented with how contrast varieties impact the panoptic segmentation performance using the MaskDINO model, the first on the panoptic quality (PQ) leaderboard. We then improved the model generalization on the various contrasts by re-optimizing it using a contrast-augmented dataset.