Systems and methods for 3D image distification
摘要
說明書
In other embodiments, the 3D imagery, and rules defining the 3D point cloud, are obtained from one or more respective PLY files or PCD files. The 3D imagery may be a frame from a 3D movie. The 3D images may be obtained from various computing devices, including, for example, any of a camera computing device, a sensor computing device, a scanner computing device, a smart phone computing device, or a tablet computing device.
In other embodiments, Distification can be executed in parallel such that the computing device, or various networked computing devices, can Distify multiple 3D images at the same time.
Distification can be performed, for example, as a preprocessing technique for a variety of applications, for example, for use with 3D predictive models. For example, systems and methods are disclosed herein for generating an image-based prediction model. As described, a computing device may obtain a set of one or more 3D images from a 3D image data source, where each of the 3D images are associated with 3D point cloud data. In some embodiments, the 3D image data source is a remote computing device (but it can also be collocated). The Distification process can be applied to the 3D point cloud data of each 3D image to generate output feature vector(s) associated with the 3D images. A prediction model may then be generated by training a model with the output feature vectors. For example, in certain embodiments, the prediction model may be trained using a neural network, such as a convolutional neural network.
In some embodiments, training the prediction model can include using one or more batches of output feature vectors, where batches of the output feature vectors correspond to one or more subsets of 3D images from originally obtained 3D images.
