# uScenes: A Multimodal RGB and 3D Sonar Dataset for Underwater Robot Perception

arXiv: https://arxiv.org/abs/2608.27795 · HTML: https://mlsanity.com/paper/2608.27795/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 30 references that are in the map.
- First posted on arXiv 28 Aug 2026.

## Builds on (most cited first, 11 of 11)

- [A Dataset with Multibeam Forward-Looking Sonar for Underwater Object Detection](https://mlsanity.com/paper/2212.00352/) (2022)
- [OceanSim: A GPU-Accelerated Underwater Robot Perception Simulation Framework](https://mlsanity.com/paper/2503.01074/) (2025)
- [USIS16K: High-Quality Dataset for Underwater Salient Instance Segmentation](https://mlsanity.com/paper/2506.19472/) (2025)
- [OysterSim: Underwater Simulation for Enhancing Oyster Reef Monitoring](https://mlsanity.com/paper/2209.09395/) (2022)
- [Embodied Foundation Models at the Edge: A Survey of Deployment Constraints and Mitigation Strategies](https://mlsanity.com/paper/2603.16952/) (2026)
- [Detecting and Counting Oysters](https://mlsanity.com/paper/2105.09758/) (2021)
- [RGB-Sonar Tracking Benchmark and Spatial Cross-Attention Transformer Tracker](https://mlsanity.com/paper/2406.07189/) (2024)
- [The Marine Debris Forward-Looking Sonar Datasets](https://mlsanity.com/paper/2503.22880/) (2025)
- [Post Fusion Bird's Eye View Feature Stabilization for Robust Multimodal 3D Detection](https://mlsanity.com/paper/2603.05623/) (2026)
- [Underwater Dense Mapping with the First Compact 3D Sonar](https://mlsanity.com/paper/2510.18991/) (2025)
- [Performance Assessment of Feature Detection Methods for 2-D FS Sonar Imagery](https://mlsanity.com/paper/2409.07004/) (2024)

## Built on by (most cited first, 2 of 2)

- [AquaBEV: Monocular Underwater BEV Occupancy with 3D Sonar Supervision](https://mlsanity.com/paper/2609.04411/) (2026)
- [AquaBEV-Nav: Learned BEV Occupancy for Underwater Navigation and Exploration](https://mlsanity.com/paper/2609.32156/) (2026)

## Abstract (arXiv)

Robust perception is essential for the deployment of autonomous underwater robots. However, optical cameras become unreliable under poor illumination and backscatter. Forward looking (2D) acoustic sensors remain effective under these conditions, but they measure range and bearing while leaving elevation unresolved, creating an ambiguity that prevents individual sonar returns from being localized in three dimensional (3D) space. This complicates the sensor use for 3D scene understanding and precise object detection. We introduce uScenes, a multimodal underwater dataset containing synchronized 3D multibeam sonar point clouds and RGB imagery. The dataset contains 110 scenes and 95,834 synchronized observation, representing 277.6 minutes of data collected across multiple field sessions. uScenes establishes a foundation for underwater sensor fusion, cross modal representation learning and 3D scene understanding. Code and datasets are given at https://github.com/era-research-lab/uScenes.