  In this paper, we present a method for creating high-quality 3D models of
sorghum panicles for phenotyping in breeding experiments. This is achieved with
a novel reconstruction approach that uses seeds as semantic landmarks in both
2D and 3D. To evaluate the performance, we develop a new metric for assessing
the quality of reconstructed point clouds without having a ground-truth point
cloud. Finally, a counting method is presented where the density of seed
centers in the 3D model allows 2D counts from multiple views to be effectively
combined into a whole-panicle count. We demonstrate that using this method to
estimate seed count and weight for sorghum outperforms count extrapolation from
2D images, an approach used in most state of the art methods for seeds and
grains of comparable size.
