An agricultural working machine, in particular a tractor, has at least one agricultural working unit for working a crop field including a multitude of useful plants, and a camera system which includes a 3D camera. The camera system is configured for generating with the 3D camera 3D information regarding the crop field by recording stereoscopic image pairs along two different viewing axes. The viewing axes proceed from optical centers of the 3D camera, which are connected to each other by a baseline that is inclined relative to the horizontal.

An image pickup system includes: an image pickup section including a first image pickup device outputting a first image pickup signal, and a second image pickup device outputting a second image pickup signal; a processor that performs signal processing on image pickup signals; and a memory section holding a difference correction parameter, the difference correction parameter indicating difference of image characteristics derived from sensitivity of each image pickup device, in image pickup signals outputted from the first image pickup device and the second image pickup device; and a correction processing section performing correction processing on at least one of the first image pickup signal and the second image pickup signal, based on the difference correction parameter.

An electronic device for processing images is provided. The electronic device includes multiple camera groups each including a pair of cameras disposed in different optical axis directions, and having different fields of views, multiple processors each configured to process images acquired through the multiple camera groups and a designated processor configured to control the first processor and the second processor. The designated processor is further configured to select an operation mode associated with the electronic device, at least based on an input associated with a first camera group or a second camera group, and if the operation mode is selected as a first operation mode, obtain an image covering the first FOV by using first images that is processed through a first processor, and restrict power supply to the second processor or the second camera group.

An electronic device for processing images is provided. The electronic device includes multiple camera groups each including a pair of cameras disposed in different optical axis directions, and having different fields of views, multiple processors each configured to process images acquired through the multiple camera groups and a designated processor configured to control the first processor and the second processor. The designated processor is further configured to select an operation mode associated with the electronic device, at least based on an input associated with a first camera group or a second camera group, and if the operation mode is selected as a first operation mode, obtain an image covering the first FOV by using first images that is processed through a first processor, and restrict power supply to the second processor or the second camera group.

The disclosure relates to systems, methods, and devices for determining a depth map of an environment based on a monocular image. A method for determining a depth map includes receiving a plurality of images from a monocular camera forming an image sequence. The method includes determining pose vector data for two successive images of the image sequence and providing the image sequence and the pose vector data to a generative adversarial network (GAN), wherein the GAN is trained using temporal constraints to generate a depth map for each image of the image sequence. The method includes generating a reconstructed image based on a depth map received from the GAN.

The disclosure relates to systems, methods, and devices for determining a depth map of an environment based on a monocular image. A method for determining a depth map includes receiving a plurality of images from a monocular camera forming an image sequence. The method includes determining pose vector data for two successive images of the image sequence and providing the image sequence and the pose vector data to a generative adversarial network (GAN), wherein the GAN is trained using temporal constraints to generate a depth map for each image of the image sequence. The method includes generating a reconstructed image based on a depth map received from the GAN.

An example method includes setting an exposure time of a camera of a distance sensor to a first value, instructing the camera to acquire a first image of an object in a field of view of the camera, where the first image is acquired while the exposure time is set to the first value, instructing a pattern projector of the distance sensor to project a pattern of light onto the object, setting the exposure time of the camera to a second value that is different than the first value, and instructing the camera to acquire a second image of the object, where the second image includes the pattern of light, and where the second image is acquired while the exposure time is set to the second value.

An example method includes setting an exposure time of a camera of a distance sensor to a first value, instructing the camera to acquire a first image of an object in a field of view of the camera, where the first image is acquired while the exposure time is set to the first value, instructing a pattern projector of the distance sensor to project a pattern of light onto the object, setting the exposure time of the camera to a second value that is different than the first value, and instructing the camera to acquire a second image of the object, where the second image includes the pattern of light, and where the second image is acquired while the exposure time is set to the second value.

A method includes obtaining a spatial configuration of a plurality of imaging devices relative to one another and to a movable object. The imaging devices are coupled to the movable object and comprise a first imaging device configured to operate in a multi-ocular mode and a second imaging device configured to operate in a monocular mode. The method further includes determining at least one of a distance of the movable object to an object or surface lying within a field-of-view of at least one of the imaging devices, a disparity between matched points in stereoscopic images acquired by the first imaging device, or an environment in which the plurality of imaging devices are operated. The distance is determined based in part on the spatial configuration. The method also includes selecting either the first imaging device or the second imaging device to acquire image data based on the determination.

A method includes obtaining a spatial configuration of a plurality of imaging devices relative to one another and to a movable object. The imaging devices are coupled to the movable object and comprise a first imaging device configured to operate in a multi-ocular mode and a second imaging device configured to operate in a monocular mode. The method further includes determining at least one of a distance of the movable object to an object or surface lying within a field-of-view of at least one of the imaging devices, a disparity between matched points in stereoscopic images acquired by the first imaging device, or an environment in which the plurality of imaging devices are operated. The distance is determined based in part on the spatial configuration. The method also includes selecting either the first imaging device or the second imaging device to acquire image data based on the determination.