Currently, computing devices, particularly those associated with machine learning for autonomous and semi-autonomous vehicles, store three dimensional (3D) models that are used as training data for machine learning. This type of training data is computationally expensive and requires a large amount of storage space. As such, it may be difficult for on-board vehicle processors to effectively utilize the training data.