1. Chebrolu, N., Magistri, F., Läbe, T., Stachniss, C.: Registration of spatio-temporal point clouds of plants for phenotyping. PLoS ONE16(2), e0247243 (2021)

   Article  Google Scholar 

2. Chéné, Y., et al.: On the use of depth camera for 3d phenotyping of entire plants. Comput. Electron. Agric.82, 122–127 (2012)

   Article  Google Scholar 

3. Cohen, J.: A coefficient of agreement for nominal scales. Educ. Psychol. Measur.20(1), 37–46 (1960)

   Article  Google Scholar 

4. BO Community: Blender - a 3D modelling and rendering package. Blender Foundation (2018).http://www.blender.org

5. Dai, A., Chang, A.X., Savva, M., Halber, M., Funkhouser, T., Nießner, M.: ScanNet: richly-annotated 3D reconstructions of indoor scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5828–5839 (2017)

   Google Scholar 

6. Emmi, L., Gonzalez-De-Santos, P.: Mobile robotics in arable lands: current state and future trends. In: 2017 European Conference on Mobile Robots, ECMR 2017 (2017).https://doi.org/10.1109/ECMR.2017.8098694

7. Griffiths, D., Boehm, J.: Weighted point cloud augmentation for neural network training data class-imbalance. arXiv preprintarXiv:1904.04094 (2019)

8. Le Louedec, J., Li, B., Cielniak, G., et al.: Evaluation of 3D vision systems for detection of small objects in agricultural environments. In: Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2020)

   Google Scholar 

9. Le Louedec, J., Montes, H.A., Duckett, T., Cielniak, G.: Segmentation and detection from organised 3D point clouds: a case study in broccoli head detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 64–65 (2020)

   Google Scholar 

10. Li, D., et al.: An overlapping-free leaf segmentation method for plant point clouds. IEEE Access7, 129054–129070 (2019)

    Article  Google Scholar 

11. Ma, X., Wang, Z., Li, H., Zhang, P., Ouyang, W., Fan, X.: Accurate monocular 3D object detection via color-embedded 3D reconstruction for autonomous driving. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 6851–6860 (2019)

    Google Scholar 

12. Magistri, F., Chebrolu, N., Stachniss, C.: Segmentation-based 4D registration of plants point clouds for phenotyping. IROS (2020)

    Google Scholar 

13. Nguyen, T.T., Slaughter, D.C., Max, N., Maloof, J.N., Sinha, N.: Structured light-based 3D reconstruction system for plants. Sensors15(8), 18587–18612 (2015)

    Article  Google Scholar 

14. Qi, C.R., Yi, L., Su, H., Guibas, L.J.: PointNet++: deep hierarchical feature learning on point sets in a metric space. In: Advances in Neural Information Processing Systems, pp. 5099–5108 (2017)

    Google Scholar 

15. Shi, W., van de Zedde, R., Jiang, H., Kootstra, G.: Plant-part segmentation using deep learning and multi-view vision. Biosyst. Eng.187, 81–95 (2019)

    Article  Google Scholar 

16. Tardieu, F., Cabrera-Bosquet, L., Pridmore, T., Bennett, M.: Plant phenomics, from sensors to knowledge. Curr. Biol.27(15), R770–R783 (2017)

    Article  Google Scholar 

17. Weber, J., Penn, J.: Creation and rendering of realistic trees. In: Proceedings of the 22nd Annual Conference on Computer Graphics and Interactive Techniques - SIGGRAPH 1995 (1995).https://doi.org/10.1145/218380.218427

18. Xia, C., Wang, L., Chung, B.K., Lee, J.M.: In situ 3D segmentation of individual plant leaves using a RGB-D camera for agricultural automation. Sensors15(8), 20463–20479 (2015)

    Article  Google Scholar 

Download references