# SPADE: Sparsity Adaptive Depth Estimator for Zero-Shot, Real-Time, Monocular Depth Estimation in Underwater Environments

arXiv: https://arxiv.org/abs/2510.25463 · HTML: https://mlsanity.com/paper/2510.25463/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 29 references that are in the map.
- First posted on arXiv 29 Oct 2025.

## Builds on (most cited first, 23 of 23)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions](https://mlsanity.com/paper/2102.12122/) (2021)
- [Vision Transformers for Dense Prediction](https://mlsanity.com/paper/2103.13413/) (2021)
- [TartanAir: A Dataset to Push the Limits of Visual SLAM](https://mlsanity.com/paper/2003.14338/) (2020)
- [VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimator](https://mlsanity.com/paper/1708.03852/) (2017)
- [Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation](https://mlsanity.com/paper/2312.02145/) (2023)
- [Non-Local Spatial Propagation Network for Depth Completion](https://mlsanity.com/paper/2007.10042/) (2020)
- [MiDaS v3.1 -- A Model Zoo for Robust Monocular Relative Depth Estimation](https://mlsanity.com/paper/2307.14460/) (2023)
- [CompletionFormer: Depth Completion with Convolutions and Vision Transformers](https://mlsanity.com/paper/2304.13030/) (2023)
- [Marigold-DC: Zero-Shot Monocular Depth Completion with Guided Diffusion](https://mlsanity.com/paper/2412.13389/) (2024)
- [Atlantis: Enabling Underwater Depth Estimation with Stable Diffusion](https://mlsanity.com/paper/2312.12471/) (2023)
- [Sparsity Agnostic Depth Completion](https://mlsanity.com/paper/2212.00790/) (2022)
- [Depth Prompting for Sensor-Agnostic Depth Estimation](https://mlsanity.com/paper/2405.11867/) (2024)
- [FLSea: Underwater Visual-Inertial and Stereo-Vision Forward-Looking Datasets](https://mlsanity.com/paper/2302.12772/) (2023)
- [DAT++: Spatially Dynamic Vision Transformer with Deformable Attention](https://mlsanity.com/paper/2309.01430/) (2023)
- [Monocular Visual-Inertial Depth Estimation](https://mlsanity.com/paper/2303.12134/) (2023)
- [Revisiting Depth Completion from a Stereo Matching Perspective for Cross-domain Generalization](https://mlsanity.com/paper/2312.09254/) (2023)
- [Metrically Scaled Monocular Depth Estimation through Sparse Priors for Underwater Robots](https://mlsanity.com/paper/2310.16750/) (2023)
- [SparseDC: Depth Completion from sparse and non-uniform inputs](https://mlsanity.com/paper/2312.00097/) (2023)
- [Hybrid Visual SLAM for Underwater Vehicle Manipulator Systems](https://mlsanity.com/paper/2112.03826/) (2021)

## Built on by (most cited first, 1 of 1)

- [Long-Term Visual Localization in Dynamic Benthic Environments: A Dataset, Footprint-Based Ground Truth, and Visual Place Recognition Benchmark](https://mlsanity.com/paper/2603.04056/) (2026)

## Abstract (arXiv)

Underwater infrastructure requires frequent inspection and maintenance due to harsh marine conditions. Current reliance on human divers or remotely operated vehicles is limited by perceptual and operational challenges, especially around complex structures or in turbid water. Enhancing the spatial awareness of underwater vehicles is key to reducing piloting risks and enabling greater autonomy. To address these challenges, we present SPADE: SParsity Adaptive Depth Estimator, a monocular depth estimation pipeline that combines pre-trained relative depth estimator with sparse depth priors to produce dense, metric scale depth maps. Our two-stage approach first scales the relative depth map with the sparse depth points, then refines the final metric prediction with our proposed Cascade Conv-Deformable Transformer blocks. Our approach achieves improved accuracy and generalisation over state-of-the-art baselines and runs efficiently at over 15 FPS on embedded hardware, promising to support practical underwater inspection and intervention. This work has been submitted to IEEE Journal of Oceanic Engineering Special Issue of AUV 2026.