[Submitted on 13 Oct 2026]
Title:Benchmarking 3D Reconstruction for Under-Ice Robotic Perception in Arctic Environments
View a PDF of the paper titled Benchmarking 3D Reconstruction for Under-Ice Robotic Perception in Arctic Environments, by Kl\'ara Orb\'an and 7 other authors
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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.
Comments: 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
Subjects:
Robotics (cs.RO)
Cite as: 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)
Submission history
From: Klara Orban [view email] [v1] Tue, 13 Oct 2026 19:34:55 UTC (872 KB)
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