Systems and methods for 3D image distification
摘要
說明書
Accordingly, various embodiments of the present disclosure relate to “Distifying” 3D imagery. In certain embodiments, the term “Distify” or “Distification” can refer to a 3D image pre-processing or normalization technique that transforms non-standardized or unstructured 3D imagery or 3D image data, such as 3D point cloud data, into a normalized set of uniform points that can be easily compared and used in a variety of applications, including machine learning, predictive models or other applications. 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. Accordingly, Distification can be used to align data points in such a way that they can be comparable and usable by in a variety of applications. In other embodiments, “Distification” refers to data alignment and interpolation of 3D images or 3D image data, such as 3D Point cloud data, the output of which can be used, for example, to compare against 2D data from other sources, as further described herein.
