Nave physics , or folk physics, is our ability to understand physical phenomena. We regularly use this ability in life to avoid collisions in trafic, follow a tennis ball and time the return shot, or while working in dynamic industrial settings. Though this skill improves with practice, it is still imperfect, which leads to mistakes and misjudgments for time intensive tasks. People still often miss a tennis shot, which might cause them to lose the match, or fail to avoid a car or pedestrian, which can lead to injury or even death. As a step towards reducing these errors in human judgement, we present Laplacian Vision (LV), a vision augmentation system which assists the human ability to predict future trajectory information. By tracking real world objects and estimating their trajectories, we can improve a users's prediction of the landing spot of a ball or the path of an oncoming car. We have designed a system that can track a ying ball in real time, predict its future trajectory, and visualize it in the user's field of view. The system is also calibrated to account for end- To-end delays so that the trajectory appears to emanate forward from the moving object. We also conduct a user study where 29 subjects predict an object's landing spot, and show that prediction accuracy improves 3 fold using LV.