In the world of automated driving, sensing accuracy is of course of the utmost importance, and proving that your sensors can do the job is serious business. This is where ground-truth labeling has an important role in Autoliv’s validation process. Currently annotating ground-truth data is a tedious manual effort, involving finding the important events of interest and using the human eye to determine objects from LiDAR point cloud images. We present a tool we developed in MATLAB to alleviate some of the pains associated with labeling point-cloud data from a LiDAR sensor and the advantages that tool provides to the labeler. We discuss the capabilities of the tool to assist users in visualizing, navigating and annotating objects in point-cloud data, tracking these objects through time over multiple frames and then using the labeled data for developing machine-learning based classifiers. The output of which provides an automated way to produce vehicle objects of interest which can be used to find false-negative events. To do this with a human analyst takes as much time as to replay back the entire data set. However, with a fully automated approach it can be run on many computers to reduce the analysis time. We present this time savings as well as the accuracy of the labels achieved and show how this approach provides substantial benefit to Autoliv’s validation process.