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Object-centric representations enable autonomous driving algorithms to reasonabout interactions between many independent agents and scene features.Traditionally these representations have been obtained via supervised learning,but this decouples perception from the downstream driving task and could harmgeneralization. In this work we adapt a self-supervised object-centric visionmodel to perform object decomposition using only RGB video and the pose of thevehicle as inputs. We demonstrate that our method obtains promising results onthe Waymo Open perception dataset. While object mask quality lags behindsupervised methods or alternatives that use more privileged information, wefind that our model is capable of learning a representation that fuses multiplecamera viewpoints over time and successfully tracks many vehicles andpedestrians in the dataset. Code for our model is available athttps://github.com/wayveai/SOCS.

Linking vision and motion for self-supervised object-centric perception