  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.
