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In this paper, we address the problem of sim-to-real transfer for objectsegmentation when there is no access to real examples of an object of interestduring training, i.e. zero-shot sim-to-real transfer for segmentation. We focuson the application of shipwreck segmentation in side scan sonar imagery. Ournovel segmentation network, STARS, addresses this challenge by fusing apredicted deformation field and anomaly volume, allowing it to generalizebetter to real sonar images and achieve more effective zero-shot sim-to-realtransfer for image segmentation. We evaluate the sim-to-real transfercapabilities of our method on a real, expert-labeled side scan sonar dataset ofshipwrecks collected from field work surveys with an autonomous underwatervehicle (AUV). STARS is trained entirely in simulation and performs zero-shotshipwreck segmentation with no additional fine-tuning on real data. Our methodprovides a significant 20% increase in segmentation performance for thetargeted shipwreck class compared to the best baseline.

STARS: Zero-shot Sim-to-Real Transfer for Segmentation of Shipwrecks in Sonar Imagery