Systems and methods for 3D image distification
摘要
說明書
Moreover, certain embodiments herein further address that each 3D file, even files using the same format, e.g., a PLY file, can include sequences of 3D data points in different, unstructured orders, such that the sequencing of 3D points of one 3D file can be different from the sequencing of 3D points of another file. This unstructured nature can create an issue when analyzing 3D images, especially when analyzing a series of 3D images, for example, from frames of a 3D movie, because there is no uniform structure to comparatively analyze the 3D images against.
For the foregoing reasons, systems and methods are disclosed herein for “Distification” of 3D imagery. As further described herein, Distification can provide an improvement in the accuracy of predictive models, such as the prediction models disclosed herein, over known normalization methods. For example, the use of Distification on 3D image data can improve the predictive accuracy, classification ability, and operation of a predictive model, even when used in known or existing predictive models, neural networks or other predictive systems and methods.
