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

arXiv: https://arxiv.org/abs/2610.19097 · HTML: https://mlsanity.com/paper/2610.19097/

## Key facts

- No citing papers in the map yet (data through 19 Oct 2026).
- Placed in Underwater scene reconstruction, one of 15 directions in 3D reconstruction and neural rendering.
- Builds on 7 of its 24 references that are in the map.
- First posted on arXiv 13 Oct 2026.

## Placed in

- 3D reconstruction and neural rendering / [Underwater scene reconstruction](https://mlsanity.com/recon3d/underwater-scene-reconstruction/)

## Builds on (most cited first, 7 of 7)

- [Depth Anything 3: Recovering the Visual Space from Any Views](https://mlsanity.com/paper/2511.10647/) (2025)
- [UNav-Sim: A Visually Realistic Underwater Robotics Simulator and Synthetic Data-generation Framework](https://mlsanity.com/paper/2310.11927/) (2023)
- [OceanSim: A GPU-Accelerated Underwater Robot Perception Simulation Framework](https://mlsanity.com/paper/2503.01074/) (2025)
- [StereoAdapter: Adapting Stereo Depth Estimation to Underwater Scenes](https://mlsanity.com/paper/2509.16415/) (2025)
- [Self-Supervised Monocular Depth Underwater](https://mlsanity.com/paper/2210.03206/) (2022)
- [A Preview of HoloOcean 2.0](https://mlsanity.com/paper/2510.06160/) (2025)
- [Underwater Monocular Metric Depth Estimation: Real-World Benchmarks and Synthetic Fine-Tuning with Vision Foundation Models](https://mlsanity.com/paper/2507.02148/) (2025)

## Abstract (arXiv)

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.