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This paper establishes a critical benchmark for vision-based 3D reconstruction of submerged ice, addressing the limitations of traditional sonar by integrating Structure-From-Motion and monocular depth estimation with acoustic data. By leveraging real-world MOSAiC expedition data alongside synthetic simulations, it provides a robust evaluation framework for perception algorithms operating in degraded Arctic sensing conditions.
Benchmarking 3D Reconstruction for Under-Ice Robotic Perception in Arctic Environments
arXiv:2610.19097v1 Announce Type: new Abstract: As the Arctic transitions toward a seasonally ice-free state, capturing the complex morphology of submerged sea ice is crucial for advancing models of ocean-ice-atmosphere heat exchange and monitoring under-ice habitats. Currently, sub-surface ice mapping is predominantly achieved through acoustic sensors such as multibeam sonar. While sonar provides robust large-scale geometry, it lacks spectral and high-resolution textural information required to record fine morphological features - a critical limitation in under-ice environments, where sparse visual features and degraded sensing conditions challenge the capture of detailed structural and optical surface properties. We present a benchmark for vision-based under-ice 3D reconstruction using real-world data from the MOSAiC expedition and synthetic data generated in an Unreal Engine-based underwater robotics simulator. The benchmark evaluates Structure-From-Motion (SfM) and monocular depth estimation, with quantitative comparison enabled through cross-modal registration to upward-looking multibeam sonar.
Abstract:As the Arctic transitions toward a seasonally ice-free state, capturing the complex morphology of submerged sea ice is crucial for advancing models of ocean-ice-atmosphere heat exchange and monitoring under-ice habitats. Currently, sub-surface ice mapping is predominantly achieved through acoustic sensors such as multibeam sonar. While sonar provides robust large-scale geometry, it lacks spectral and high-resolution textural information required to record fine morphological features - a critical limitation in under-ice environments, where sparse visual features and degraded sensing conditions challenge the capture of detailed structural and optical surface properties. We present a benchmark for vision-based under-ice 3D reconstruction using real-world data from the MOSAiC expedition and synthetic data generated in an Unreal Engine-based underwater robotics simulator. The benchmark evaluates Structure-From-Motion (SfM) and monocular depth estimation, with quantitative comparison enabled through cross-modal registration to upward-looking multibeam sonar.
Presented at the ICRA 2026 Workshop "From Sea to Space: Advancing Perception in Harsh Domains" (S2S), Vienna, Austria, June 1, 2026. 8 figures, 2 tables. Workshop: this https URL
Robotics (cs.RO)
arXiv:2610.19097 [cs.RO]
(or arXiv:2610.19097v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2610.19097 arXiv-issued DOI via DataCite (pending registration)
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