3D Point Cloud on Semantic Information for Wheat Reconstruction

Phenotypic analysis has always played an important role in breeding research. At present, wheat phenotypic research mostly relies on high-precision instruments, which make the cost higher. Thanks to development of 3D reconstruction technology, reconstructed model can also be used for analysis. In this paper, a method is proposed reconstruct based semantic information. The generate corresponding point cloud according description. First, object detection algorithm detect characteristics some phenotypes during growth process. Second, environment information and features are combined into Third, text-to-image 2D image wheat. Finally, transformed abstract obtained higher precision using deep learning algorithm. Extensive experiments indicate that reconstructs models heuristic effect by learning.