Abstract: An image rendering system for rendering two-dimensional images in real-time. The image rendering system can receive an implicit representation model of a three-dimensional image. The image rendering system can construct, based on voxel coordinates, a three-dimensional image based on the implicit representation model. The image rendering system can rotate the three-dimensional image to an orientation in a computing space based on a user input. The image rendering system can generate a two-dimensional image based on the rotated three-dimensional image.

Abstract: Described herein are methods and non-transitory computer-readable media configured to obtain a plurality of images from a plurality of image scanning orientations for an object. A rigid registration is performed to the plurality of images to obtain a transformation matrix to normalize the plurality of images from their respective image spaces to a normalized image space. Each normalized image comprises a plurality of voxels. A machine learning model comprising an implicit representation of a high-resolution image is trained using the normalized images, wherein the high-resolution image comprises more voxels than the voxels in the normalized images. The high-resolution image is generated based on the trained machine learning model. The plurality of images are a plurality of anisotropic 2D images, while the high resolution image can be a 2D or 3D high resolution image.

Abstract: The present invention provides a panoramic video frame interpolation method, including outputting a pre-post frame image optical flow graph and a post-pre frame image optical flow graph; calculating an image optical flow graph before frame interpolation and an image optical flow graph after frame interpolation; obtaining a downsampling pre-frame image after transformation and a downsampling post-frame image after transformation; outputting a pre-post frame image correction optical flow graph, a post-pre frame image correction optical flow graph and an image occlusion relationship graph; obtaining a downsampling pre-frame image after correction and a downsampling post-frame image after correction; obtaining an upsampling image optical flow graph before frame interpolation and an upsampling image optical flow graph after frame interpolation; and calculating a frame interpolation image corresponding to a frame interpolation position.

Abstract: The present invention provides a binocular image quick processing method, including: performing feature extraction on a next-level left eye image and a next-level right eye image; acquiring a next-level image phase difference distribution estimation feature; fusing the next-level image phase difference distribution estimation feature and a next-level left eye image feature to obtain a next-level fusion feature; performing feature extraction on the next-level fusion feature to obtain a difference feature of next-level left and right eye images, and obtain an estimated phase difference of the next-level left and right eye images; acquiring an estimated phase difference of first-level left and right eye images; and performing processing operation on the corresponding images by using the estimated phase difference of the first-level left and right eye images.

Abstract: The disclosure provides a model generation method based on a multi-view panoramic image, including: calculating an image rectification rotation matrix of source images and a reference image; extracting a reference image feature of the reference image and source image features of the source images; performing a fusion operation on rectified cost volumes of the plurality of source images corresponding to the reference image to obtain a final cost volume; calculating an estimated phase difference under a set resolution; obtaining a final phase difference of the reference image; and generating a depth map of the reference image, and constructing a corresponding stereo vision model.