Two Teachers Are Better Than One: Leveraging Depth In Training Only For Unsupervised Obstacle Segmentation

Sungmin Eum, Hyungtae Lee, Heesung Kwon, Philip R. Osteen, Andre Harrison

Abstract: We present a novel unsupervised obstacle segmentation architecture that follows a novel Relation Distillation (RD) paradigm. Our architecture design was inspired by a self-supervised teacher-student approach that relies on the Semantic Distillation originally devised for representation learning. While the teacher in the Semantic Distillation considers a single patch at a time, the teacher within RD takes a ‘pair of patches’ instead to transfer the local Semantic Co-occurrence Localization (SCooL) relationship that focuses more on the segmentation-boosting signals. To further improve the proposed architecture, we introduce the utilization of another teacher that leverages the depth information which inherently separates the entities at different physical distances, often tied with the boundaries of the obstacles. As the depth is distilled towards the student network only at the time of training, it adds zero computational/hardware cost at run-time. As no relevant public dataset is available, we have curated the Avoiding Obstacles In unstructured Driving (AvOID) dataset as a new testbed for unsupervised obstacle segmentation. We have validated that both the Relation Distillation and depth contribute to boosting the no-annotation segmentation performance on AvOID and KITTI-Obstacles.