Recovering and analyzing 3D models of branched structures using computer vision: a review
Proceedings of the 27th Conference on Image and Vision Computing New Zealand
Reconstructing partially visible models using stereo vision, structured light, and the g2o framework
Proceedings of the 27th Conference on Image and Vision Computing New Zealand
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One of the main challenges in high-throughput plant data acquisition is the robust and automated analysis of the data. This includes a high-resolution 3D plant model reconstruction and an automated 3D segmentation. In this paper we present our top-down partitioning pipeline used to automatically segment high-resolution plant meshes. The proposed method produces a smart partition of the initial mesh that allows to identify the main stem, branches, and leaves of the plant. Extracted regions are then processed through the next stage of the automated analysis, which retrieves accurate plant information such as stem length, leaf width, length or area. Results involved applying our top-down approach on a prototype population of 6 cotton-plant meshes studied at 3 or 4 time points. Using our partitioning pipeline, we obtained accurate meshes segmentations for 20 plants out of the initial 22. Results validate the feasibility of an automated analysis of plant data. Future work will involve extending our approach to multiple plant varieties and using an atlas-based iterative feedback scheme to improve the 3D plant reconstruction.