# Absolute 3D Pose Estimation and Length Measurement of Severely Deformed Fish from Monocular Videos in Longline Fishing

arXiv: https://arxiv.org/abs/2102.04639 · HTML: https://mlsanity.com/paper/2102.04639/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 22 references that are in the map.
- First posted on arXiv 9 Feb 2021.

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

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [An overview of gradient descent optimization algorithms](https://mlsanity.com/paper/1609.04747/) (2016)
- [A Point Set Generation Network for 3D Object Reconstruction from a Single Image](https://mlsanity.com/paper/1612.00603/) (2016)
- [Pixel2Mesh: Generating 3D Mesh Models from Single RGB Images](https://mlsanity.com/paper/1804.01654/) (2018)
- [GEOMetrics: Exploiting Geometric Structure for Graph-Encoded Objects](https://mlsanity.com/paper/1901.11461/) (2019)
- [Video-based Hierarchical Species Classification for Longline Fishing Monitoring](https://mlsanity.com/paper/2102.03520/) (2021)

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

- [Unsupervised Severely Deformed Mesh Reconstruction (DMR) from a Single-View Image](https://mlsanity.com/paper/2201.09373/) (2022)
- [HCIL: Hierarchical Class Incremental Learning for Longline Fishing Visual Monitoring](https://mlsanity.com/paper/2202.13018/) (2022)
- [ProTPS: Prototype-Guided Text Prompt Selection for Continual Learning](https://mlsanity.com/paper/2604.01116/) (2026)

## Abstract (arXiv)

Monocular absolute 3D fish pose estimation allows for efficient fish length measurement in the longline fisheries, where fishes are under severe deformation during the catching process. This task is challenging since it requires locating absolute 3D fish keypoints based on a short monocular video clip. Unlike related works, which either require expensive 3D ground-truth data and/or multiple-view images to provide depth information, or are limited to rigid objects, we propose a novel frame-based method to estimate the absolute 3D fish pose and fish length from a single-view 2D segmentation mask. We first introduce a relative 3D fish template. By minimizing an objective function, our method systematically estimates the relative 3D pose of the target fish and fish 2D keypoints in the image. Finally, with a closed-form solution, the relative 3D fish pose can help locate absolute 3D keypoints, resulting in the frame-based absolute fish length measurement, which is further refined based on the statistical temporal inference for the optimal fish length measurement from the video clip. Our experiments show that this method can accurately estimate the absolute 3D fish pose and further measure the absolute length, even outperforming the state-of-the-art multi-view method.