# Improved Image-based Pose Regressor Models for Underwater Environments

arXiv: https://arxiv.org/abs/2403.08360 · HTML: https://mlsanity.com/paper/2403.08360/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 10 references that are in the map.
- First posted on arXiv 13 Mar 2024.

## Builds on (most cited first, 4 of 4)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Geometric Loss Functions for Camera Pose Regression with Deep Learning](https://mlsanity.com/paper/1704.00390/) (2017)
- [Image-based localization using LSTMs for structured feature correlation](https://mlsanity.com/paper/1611.07890/) (2016)

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

- [Pose Estimation from Camera Images for Underwater Inspection](https://mlsanity.com/paper/2407.16961/) (2024)
- [Image Compression Using Novel View Synthesis Priors](https://mlsanity.com/paper/2411.13862/) (2024)

## Abstract (arXiv)

We investigate the performance of image-based pose regressor models in underwater environments for relocalization. Leveraging PoseNet and PoseLSTM, we regress a 6-degree-of-freedom pose from single RGB images with high accuracy. Additionally, we explore data augmentation with stereo camera images to improve model accuracy. Experimental results demonstrate that the models achieve high accuracy in both simulated and clear waters, promising effective real-world underwater navigation and inspection applications.