Benchmarking 3D Reconstruction for Under-Ice Robotic Perception in Arctic Environments

Klara Orban, Ashwin Nedungadi, Ana-Ștefania Kogălniceanu, Bianca-Marcela Sava, Bastian Cieply, Raul-Alexandru Simon, Kuderna-Iulian Bența, Stefan Lüdtke

Keywords: Robotic Perception, Under-Ice Mapping, Structure-from-Motion (SfM), Multibeam Sonar, Monocular Depth Estimation, Synthetic Data Generation

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 the 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.

Email Sharing: We authorize the sharing of all author emails with Program Chairs.

Data Release: We authorize the release of our submission and author names to the public in the event of acceptance.

Paper Acceptance: No

Submission Number: 21