A method for generating a panoramic image is disclosed. The method comprises performing a cylindrical projection to project multiple camera images to cylindrical images, as a first step; and aligning overlapping regions of the cylindrical images, as a second step.

A display device of an autonomous vehicle is controlled based on data collected from sensors located in or on the vehicle. The display device is used to present one or more images to a driver and/or passengers of the autonomous vehicle. The display device can be, for example, a windshield and/or other window of the vehicle. Image data can be, for example, transformed to improve visual perception by passengers in the vehicle when the images are displayed on a curved shape of the windshield.

An image processing apparatus is configured to generate a display image to be produced on a display system including a display unit. The image processing apparatus includes an acquisition unit configured to acquire orientation information indicating an orientation of an imaging apparatus when the imaging apparatus captures an input image, a setting unit configured to set a projection plane in a virtual space based on the orientation information, and a generation unit configured to generate the display image to be produced on the display unit with use of a relationship between the input image and the projection plane.

Systems, apparatuses and methods may provide for technology that generates, by a first neural network, an initial set of model weights based on input data and iteratively generates, by a second neural network, an updated set of model weights based on residual data associated with the initial set of model weights and the input data. Additionally, the technology may output a geometric model of the input data based on the updated set of model weights. In one example, the first neural network and the second neural network reduce the dependence of the geometric model on the number of data points in the input data.

Systems, apparatuses and methods may provide for technology that generates, by a first neural network, an initial set of model weights based on input data and iteratively generates, by a second neural network, an updated set of model weights based on residual data associated with the initial set of model weights and the input data. Additionally, the technology may output a geometric model of the input data based on the updated set of model weights. In one example, the first neural network and the second neural network reduce the dependence of the geometric model on the number of data points in the input data.

Apparatus and methods for the stitch zone calculation of a generated projection of a spherical image. In one embodiment, a non-transitory computer-readable apparatus comprising a storage apparatus, the storage apparatus comprising instructions configured to, when executed by a processor apparatus, cause a computerized apparatus to identify a stitch line associated with an equatorial area of a plurality of spherical images; re-orient the plurality of spherical images in accordance with the stitch line; and project the re-oriented plurality of spherical images to a selected image projection type.

Apparatus and methods for the stitch zone calculation of a generated projection of a spherical image. In one embodiment, a non-transitory computer-readable apparatus comprising a storage apparatus, the storage apparatus comprising instructions configured to, when executed by a processor apparatus, cause a computerized apparatus to identify a stitch line associated with an equatorial area of a plurality of spherical images; re-orient the plurality of spherical images in accordance with the stitch line; and project the re-oriented plurality of spherical images to a selected image projection type.

Systems and methods are disclosed for aligning a two-dimensional (2D) design image to a 2D projection image of a three-dimensional (3D) design model. One method comprises receiving a 2D design document, the 2D design document comprising a 2D design image, and receiving a 3D design file comprising a 3D design model, the 3D design model comprising one or more design elements. The method further comprises generating a 2D projection image based on the 3D design model, the 2D projection image comprising a representation of at least a portion of the one or more design elements, generating a projection barcode based on the 2D projection image, and generating a drawing barcode based on the 2D design image. The method further comprises aligning the 2D projection image and the 2D design image by comparing the projection barcode and the drawing barcode.

Systems and methods are disclosed for aligning a two-dimensional (2D) design image to a 2D projection image of a three-dimensional (3D) design model. One method comprises receiving a 2D design document, the 2D design document comprising a 2D design image, and receiving a 3D design file comprising a 3D design model, the 3D design model comprising one or more design elements. The method further comprises generating a 2D projection image based on the 3D design model, the 2D projection image comprising a representation of at least a portion of the one or more design elements, generating a projection barcode based on the 2D projection image, and generating a drawing barcode based on the 2D design image. The method further comprises aligning the 2D projection image and the 2D design image by comparing the projection barcode and the drawing barcode.

The innovation describes and discloses systems and methods related to deep neural networks employing machine learning to detect item 2D landmark points from a single image, such as those of an image of a face, and to estimate their 3D coordinates and shape rapidly and accurately. The system also provides for mapping by a feed-forward neural network that defines two criteria, one to learn to detect important shape landmark points on the image and another to recover their depth information. An aspect of the innovation may utilize camera models in a data augmentation approach that aids machine learning of a complex, non-linear mapping function. Other augmentation approaches are also considered.