Gm Global Technolgoy Operations, Inc.

Albert Shalumov

Michael Slutsky

Methods and system for training a neural network for depth estimation in a vehicle. The methods and systems receive respective training image data from at least two cameras. Fields of view of adjacent cameras of the at least two cameras partially overlap. The respective training image data is processed through a neural network providing depth data and semantic segmentation data as outputs. The neural network is trained based on a loss function. The loss function combines a plurality of loss terms including at least a semantic segmentation loss term and a panoramic loss term. The panoramic loss term includes a similarity measure regarding overlapping image patches of the respective image data that each correspond to a region of overlapping fields of view of the adjacent cameras. The semantic segmentation loss term quantifies a difference between ground truth semantic segmentation data and the semantic segmentation data output from the neural network.

Systems and methods for depth estimation in a vehicle

Systems and methods for generating a virtual view of a virtual camera based on an input image are described. A system for generating a virtual view of a virtual camera based on an input image can include a capturing device including a physical camera and a depth sensor. The system also includes a controller configured to determine an actual pose of the capturing device; determine a desired pose of the virtual camera for showing the virtual view; define an epipolar geometry between the actual pose of the capturing device and the desired pose of the virtual camera; and generate a virtual image depicting objects within the input image according to the desired pose of the virtual camera for the virtual camera based on an epipolar relation between the actual pose of the capturing device, the input image, and the desired pose of the virtual camera.

Generating virtual images based on captured image data

Systems and methods for generating a virtual view of a virtual camera based on an input scene are described. A capturing device typically includes a physical camera and a depth sensor and captures an input scene. A controller determines an actual pose of the capturing device and a desired pose of the virtual camera for showing the virtual view. The controller defines an epipolar geometry between the actual pose of the capturing device and the desired pose of the virtual camera. The controller generates an output image for the virtual camera based on an epipolar relation between the actual pose of the capturing device, the input scene, and the desired pose of the virtual camera.

Using epipolar reprojection for virtual view perspective change

Presented are intelligent vehicle systems with networked on-body vehicle cameras with camera-view augmentation capabilities, methods for making/using such systems, and vehicles equipped with such systems. A method for operating a motor vehicle includes a system controller receiving, from a network of vehicle-mounted cameras, camera image data containing a target object from a perspective of one or more cameras. The controller analyzes the camera image to identify characteristics of the target object and classify these characteristics to a corresponding model collection set associated with the type of target object. The controller then identifies a 3D object model assigned to the model collection set associated with the target object type. A new 'virtual' image is generated by replacing the target object with the 3D object model positioned in a new orientation. The controller commands a resident vehicle system to execute a control operation using the new image.

Intelligent vehicle systems and control logic for surround view augmentation with object model recognition

Systems and methods for projecting a multi-faceted image onto a convex polyhedron based on an input image are described. A system can include a controller configured to determine a mapping between pixels within a wide-angle image and a multi-faceted image, and generate the multi-faceted image based on the mapping.

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Petah Tikwa, Israel

Albert Shalumov