3D Plant phenotyping: All you need is labelled point cloud data

Chaudhury Ayan, Boudon Frédéric, Godin Christophe. 2020. 3D Plant phenotyping: All you need is labelled point cloud data. . IPPN. Glasgow : IPPN, 1-17. ECCV 2020 Workshop on Computer Vision Problems in Plant Phenotyping (CVPPP 2020), Glasgow, Royaume-Uni, 28 Août 2020/28 Août 2020.

Note générale : Le congrès s'est tenu en ligne

Résumé : In the realm of modern digital phenotyping technological advancements, the demand of annotated datasets is increasing for either training machine learning algorithms or evaluating 3D phenotyping systems. While a few 2D datasets have been proposed in the community in last few years, very little attention has been paid to the construction of annotated 3D point cloud datasets. There are several challenges associated with the creation of such annotated datasets. Acquiring the data requires instruments having good precision and accuracy levels. Reconstruction of full 3D model from multiple views is a challenging task considering plant architecture complexity and plasticity, as well as occlusion and missing data problems. In addition, manual annotation of the data is a cumbersome task that cannot easily be automated. In this context, the design of synthetic datasets can play an important role. In this paper, we propose an idea of automatic generation of synthetic point cloud data using virtual plant models. Our approach leverages the strength of the classical procedural approach (like L-systems) to generate the virtual models of plants, and then perform point sampling on the surface of the models. By applying stochasticity in the procedural model, we are able to generate large number of diverse plant models and the corresponding point cloud data in a fully automatic manner. The goal of this paper is to present a general strategy to generate annotated 3D point cloud datasets from virtual models. The code (along with some generated point cloud models) are available at: https://gitlab.inria.fr/mosaic/publications/lpy2pc.

Auteurs et affiliations

• Chaudhury Ayan, INRIA (FRA)
• Boudon Frédéric, CIRAD-BIOS-UMR AGAP (FRA) ORCID: 0000-0001-9636-3102
• Godin Christophe, INRIA (FRA)

Source : Cirad-Agritrop (https://agritrop.cirad.fr/597186/)

Métriques

[ Page générée et mise en cache le 2022-06-02 ]