Label3DMaize: toolkit for 3D point cloud data annotation of maize shoots.

ABSTRACT:

Background

The 3D point cloud is the most direct and effective data form for studying plant structure and morphology. In point cloud studies, the point cloud segmentation of individual plants to organs directly determines the accuracy of organ-level phenotype estimation and the reliability of the 3D plant reconstruction. However, highly accurate, automatic, and robust point cloud segmentation approaches for plants are unavailable. Thus, the high-throughput segmentation of many shoots is challenging. Although deep learning can feasibly solve this issue, software tools for 3D point cloud annotation to construct the training dataset are lacking.

Results

We propose a top-to-down point cloud segmentation algorithm using optimal transportation distance for maize shoots. We apply o

SUBMITTER: Miao T 

PROVIDER: S-EPMC8105162 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature