ShrimpSeg: a local–global structure for the mantis shrimp point cloud segmentation network with contextual reasoning

Abstract

To quantify the architecture and select the ideal ideotype, it is vital to accurately measure the dimension of each part of the mantis shrimp. Point clouds have become increasingly popular in recent years as an efficient solution. However, the current manual measurement is labor intensive and costly and has high uncertainty. Automatic organ point cloud segmentation is a prerequisite and core step for phenotypic measurements of mantis shrimps. Nevertheless, little work focuses on mantis shrimp point cloud segmentation. To fill this gap, this paper develops a framework for automated organ segmentation of mantis shrimps from multiview stereo (MVS) point clouds. First, a Transformer-based MVS architecture is applied to generate dense point clouds from a set of calibrated phone images and estimated camera parameters. Next, an improved point cloud segmentation (named ShrimpSeg) that exploits both local and global features based on contextual information is proposed for organ segmentation of mantis shrimps. According to the evaluation results, the per-class intersection over union of organ-level segmentation is 82.4%. Comprehensive experiments demonstrate the effectiveness of ShrimpSeg, outperforming other commonly used segmentation methods. This work may be helpful for improving shrimp phenotyping and intelligent aquaculture at the level of production-ready.