Score based generative models are a new class of generative
models that have been shown to accurately generate high dimensional
calorimeter datasets. Recent advances in generative models have used
images with 3D voxels to represent and model complex calorimeter
showers. Point clouds, however, are likely a more natural
representation of calorimeter showers, particularly in calorimeters
with high granularity. Point clouds preserve all of the information
of the original simulation, more naturally deal with sparse
datasets, and can be implemented with more compact models and data
files. In this work, two state-of-the-art score based models are
trained on the same set of calorimeter simulation and directly
compared.