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Plant Phenomics can be used to monitor the health and the growth of plants.Computer vision applications like stereo reconstruction, image retrieval,object tracking, and object recognition play an important role in imaging basedplant phenotyping. This paper offers a comparative evaluation of some popular3D correspondence grouping algorithms, motivated by the important role thatthey can play in tasks such as model creation, plant recognition andidentifying plant parts. Another contribution of this paper is the extension of2D maximum likelihood matching to 3D Maximum Likelihood Estimation SampleConsensus (MLEASAC). MLESAC is efficient and is computationally less intensethan 3D random sample consensus (RANSAC). We test these algorithms on 3D pointclouds of plants along with two standard benchmarks addressing shape retrievaland point cloud registration scenarios. The performance is evaluated in termsof precision and recall.

Performance comparison of 3D correspondence grouping algorithm for 3D plant point clouds