# Model-Based Underwater 6D Pose Estimation from RGB

arXiv: https://arxiv.org/abs/2302.06821 · HTML: https://mlsanity.com/paper/2302.06821/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 35 references that are in the map.
- First posted on arXiv 14 Feb 2023.

## Builds on (most cited first, 21 of 21)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes](https://mlsanity.com/paper/1711.00199/) (2017)
- [PVNet: Pixel-wise Voting Network for 6DoF Pose Estimation](https://mlsanity.com/paper/1812.11788/) (2018)
- [BOP: Benchmark for 6D Object Pose Estimation](https://mlsanity.com/paper/1808.08319/) (2018)
- [Deep Object Pose Estimation for Semantic Robotic Grasping of Household Objects](https://mlsanity.com/paper/1809.10790/) (2018)
- [BB8: A Scalable, Accurate, Robust to Partial Occlusion Method for Predicting the 3D Poses of Challenging Objects without Using Depth](https://mlsanity.com/paper/1703.10896/) (2017)
- [Scaled-YOLOv4: Scaling Cross Stage Partial Network](https://mlsanity.com/paper/2011.08036/) (2020)
- [T-LESS: An RGB-D Dataset for 6D Pose Estimation of Texture-less Objects](https://mlsanity.com/paper/1701.05498/) (2017)
- [Real-Time Seamless Single Shot 6D Object Pose Prediction](https://mlsanity.com/paper/1711.08848/) (2017)
- [Implicit 3D Orientation Learning for 6D Object Detection from RGB Images](https://mlsanity.com/paper/1902.01275/) (2019)
- [HomebrewedDB: RGB-D Dataset for 6D Pose Estimation of 3D Objects](https://mlsanity.com/paper/1904.03167/) (2019)
- [Semantic Segmentation of Underwater Imagery: Dataset and Benchmark](https://mlsanity.com/paper/2004.01241/) (2020)
- [Self6D: Self-Supervised Monocular 6D Object Pose Estimation](https://mlsanity.com/paper/2004.06468/) (2020)
- [EfficientPose: An efficient, accurate and scalable end-to-end 6D multi object pose estimation approach](https://mlsanity.com/paper/2011.04307/) (2020)
- [UDepth: Fast Monocular Depth Estimation for Visually-guided Underwater Robots](https://mlsanity.com/paper/2209.12358/) (2022)
- [SilhoNet: An RGB Method for 6D Object Pose Estimation](https://mlsanity.com/paper/1809.06893/) (2018)
- [DeepURL: Deep Pose Estimation Framework for Underwater Relative Localization](https://mlsanity.com/paper/2003.05523/) (2020)
- [Underwater Robot Manipulation: Advances, Challenges and Prospective Ventures](https://mlsanity.com/paper/2201.02954/) (2022)
- [Towards Automated Sample Collection and Return in Extreme Underwater Environments](https://mlsanity.com/paper/2112.15127/) (2021)
- [SilhoNet-Fisheye: Adaptation of A ROI Based Object Pose Estimation Network to Monocular Fisheye Images](https://mlsanity.com/paper/2002.12415/) (2020)

## Built on by (most cited first, 3 of 3)

- [Challenges for Monocular 6D Object Pose Estimation in Robotics](https://mlsanity.com/paper/2307.12172/) (2023)
- [Uncovering the Background-Induced bias in RGB based 6-DoF Object Pose Estimation](https://mlsanity.com/paper/2304.08230/) (2023)
- [FAFA: Frequency-Aware Flow-Aided Self-Supervision for Underwater Object Pose Estimation](https://mlsanity.com/paper/2409.16600/) (2024)

## Abstract (arXiv)

Object pose estimation underwater allows an autonomous system to perform tracking and intervention tasks. Nonetheless, underwater target pose estimation is remarkably challenging due to, among many factors, limited visibility, light scattering, cluttered environments, and constantly varying water conditions. An approach is to employ sonar or laser sensing to acquire 3D data, however, the data is not clear and the sensors expensive. For this reason, the community has focused on extracting pose estimates from RGB input. In this work, we propose an approach that leverages 2D object detection to reliably compute 6D pose estimates in different underwater scenarios. We test our proposal with 4 objects with symmetrical shapes and poor texture spanning across 33,920 synthetic and 10 real scenes. All objects and scenes are made available in an open-source dataset that includes annotations for object detection and pose estimation. When benchmarking against similar end-to-end methodologies for 6D object pose estimation, our pipeline provides estimates that are 8% more accurate. We also demonstrate the real world usability of our pose estimation pipeline on an underwater robotic manipulator in a reaching task.