AquaBEV: Monocular Underwater BEV Occupancy with 3D Sonar Supervision

Abstract:Autonomous underwater robots are widely used for exploration, monitoring, and inspection, where safe navigation depends on understanding the surrounding free and occupied space. Bird's eye view (BEV) occupancy provides such a representation, but predicting it from a single underwater RGB image is difficult due to limited, unreliable geometric cues from appearance alone. 3D imaging sonar offers complementary geometric measurements to supervise this task. We introduce AquaBEV, a monocular underwater occupancy model that predicts local BEV occupancy from a single RGB image, using paired 3D imaging sonar as geometric supervision during training. AquaBEV maps visual features into a calibration free polar representation and applies causal decoding along the range dimension before reconstructing the prediction in Cartesian BEV coordinates. A controlled underwater occupancy benchmark was established, adapting representative occupancy methods to the same RGB to sonar task under a unified protocol. AquaBEV achieves 31.4 Visible IoU and 38.6 Observed IoU, 4.0% and 4.3% relative improvements over the strongest transferred baseline.

Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)

arXiv:2609.04411 [cs.RO]

(or arXiv:2609.04411v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2609.04411 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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