Abstract: A method for automatically predicting the cause of suboptimal shooting is provided. In some embodiments, the method comprises: providing a plurality of good example reference data to an evaluation function; providing a plurality of bad example reference data to the evaluation function; obtaining training data of a trainee's shot dispersion data; obtaining training data from at least one sensor mounted on the trainee's gun; using the evaluation function to classify the training data as good or bad; and, displaying the classification on a screen as feedback.

Abstract: Provided herein are methods and systems to provide for parallax correction for multiple eye-points in a single viewpoint within a Homogeneous 3-Dimensional (3D) virtual environment. In preferred embodiments, the systems and methods are used as part of a virtual training environment where multiple participants are participating simultaneously. The methods and systems render multiple objects from multiple eye-points by creating custom projection matrix for each eye-point. Each rendered object is overlaid over the background and other images rendered with the standard eye-point projection matrix. Preferred embodiments use the same view matrix for all renderings.

Abstract: Provided herein are methods and systems to provide for parallax correction for multiple eye-points in a single viewpoint within a Homogeneous 3-Dimensional (3D) virtual environment. In preferred embodiments, the systems and methods are used as part of a virtual training environment where multiple participants are participating simultaneously. The methods and systems render multiple objects from multiple eye-points by creating custom projection matrix for each eye-point. Each rendered object is overlaid over the background and other images rendered with the standard eye-point projection matrix. Preferred embodiments use the same view matrix for all renderings.

Abstract: A method for automatically predicting the cause of suboptimal shooting is provided. In some embodiments, the method comprises: providing a plurality of good example reference data to an evaluation function; providing a plurality of bad example reference data to the evaluation function; obtaining training data of a trainee's shot dispersion data; obtaining training data from at least one sensor mounted on the trainee's gun; using the evaluation function to classify the training data as good or bad; and, displaying the classification on a screen as feedback.