3D reconstruction method for tree seedlings based on point cloud self-registration

The 3D reconstruction of tree seedlings can help to assess phenotypic architectures, conceive virtual urban landscapes and design computer games. The existing multicamera photograph technology already has the capability to accurately reconstruct 3D models for small scene plants, such as corn and vegetable seedlings. However, the existing plant 3D reconstruction system has several shortcomings, such as its high cost, compli-cated operation procedure, and unsuitability for seedling trees. Therefore, this paper proposes an autonomous alignment method for seedling point clouds that can realize the low-cost and fast 3D reconstruction of batch seedlings. In this study, we designed a system based on a low-cost Kinect camera and a precision turntable to construct 3D seedling models. A special turntable was adopted to achieve self-registration for the seedling point clouds. It was efficient for us to obtain several 3D seedlings models with only one registration. The system could capture images automatically from different viewpoints and submit these images to a graphic workstation for processing. In our work, we set three fixed views, V2, V3 and V4, to evaluate the cumulative errors caused by multiview matching. It needn't touch any parts of the seedings to create 3D models at different view by the proposed method. Herein, the large proportions of 0 < mean absolute distance, MD <= 0.6 cm and 0 < standard deviation, SD <= 0.4 cm, between the reference and the reconstructed point cloud showed that the 3D recon-struction method was accurate, stable and flexible. Additionally, we validated the phenotypic structure mea-surement, and the height H was highly accurate (R2 > 0.985) when using the 3D reconstruction models of seedlings. Experiments demonstrate that the proposed method has the potential to obtain high-precision 3D reconstruction models and phenotypic parameters for seedlings via low-cost equipment with high-efficiency processing algorithms.

Automated summary

This study proposes an autonomous alignment method for seedling point clouds, which enables low-cost and fast 3D reconstruction of batch seedlings. By using a special turntable and a low-cost camera, accurate and stable 3D seedling models and phenotypic parameters can be obtained efficiently.