  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 based
plant phenotyping. This paper offers a comparative evaluation of some popular
3D correspondence grouping algorithms, motivated by the important role that
they can play in tasks such as model creation, plant recognition and
identifying plant parts. Another contribution of this paper is the extension of
2D maximum likelihood matching to 3D Maximum Likelihood Estimation Sample
Consensus (MLEASAC). MLESAC is efficient and is computationally less intense
than 3D random sample consensus (RANSAC). We test these algorithms on 3D point
clouds of plants along with two standard benchmarks addressing shape retrieval
and point cloud registration scenarios. The performance is evaluated in terms
of precision and recall.
