Systems and methods are provided performing for low compute depth map generation by implementing acts of obtaining a stereo pair of images of a scene, downsampling the stereo pair of images, generating a depth map by stereo matching the downsampled stereo pair of images, and generating an upsampled depth map based on the depth map using an edge-preserving filter for obtaining at least some data of at least one image of the stereo pair of images.

Systems and methods are provided performing for low compute depth map generation by implementing acts of obtaining a stereo pair of images of a scene, downsampling the stereo pair of images, generating a depth map by stereo matching the downsampled stereo pair of images, and generating an upsampled depth map based on the depth map using an edge-preserving filter for obtaining at least some data of at least one image of the stereo pair of images.

A self-contained, low-cost, low-weight guidance system for vehicles is provided. The guidance system can include an optical camera, a case, a processor, a connection between the processor and an on-board control system, and computer algorithms running on the processor. The guidance system can be integrated with a vehicle control system through “plug and play” functionality or a more open Software Development Kit. The computer algorithms re-create 3D structures as the vehicle travels and continuously updates a 3D model of the environment. The guidance system continuously identifies and tracks terrain, static objects, and dynamic objects through real-time camera images. The guidance system can receive inputs from the camera and the onboard control system. The guidance system can be used to assist vehicle navigation and to avoid possible collisions. The guidance system can communicate with the control system and provide navigational direction to the control system.

A self-contained, low-cost, low-weight guidance system for vehicles is provided. The guidance system can include an optical camera, a case, a processor, a connection between the processor and an on-board control system, and computer algorithms running on the processor. The guidance system can be integrated with a vehicle control system through “plug and play” functionality or a more open Software Development Kit. The computer algorithms re-create 3D structures as the vehicle travels and continuously updates a 3D model of the environment. The guidance system continuously identifies and tracks terrain, static objects, and dynamic objects through real-time camera images. The guidance system can receive inputs from the camera and the onboard control system. The guidance system can be used to assist vehicle navigation and to avoid possible collisions. The guidance system can communicate with the control system and provide navigational direction to the control system.

One aspect of the invention relates to a fully automatic method for calculating the current, geo-referenced position and alignment of a terrestrial scan-surveying device in situ on the basis of a current panoramic image recorded by the surveying device and at least one stored, geo-referenced 3D scan panoramic image.

One aspect of the invention relates to a fully automatic method for calculating the current, geo-referenced position and alignment of a terrestrial scan-surveying device in situ on the basis of a current panoramic image recorded by the surveying device and at least one stored, geo-referenced 3D scan panoramic image.

Described are methods for identifying the in-field positions of plant features on a plant by plant basis. These positions are determined based on images captured as a vehicle (e.g., tractor, sprayer, etc.) including one or more cameras travels through the field along a row of crops. The in-field positions of the plant features are useful for a variety of purposes including, for example, generating three-dimensional data models of plants growing in the field, assessing plant growth and phenotypic features, determining what kinds of treatments to apply including both where to apply the treatments and how much, determining whether to remove weeds or other undesirable plants, and so on.

Described are methods for identifying the in-field positions of plant features on a plant by plant basis. These positions are determined based on images captured as a vehicle (e.g., tractor, sprayer, etc.) including one or more cameras travels through the field along a row of crops. The in-field positions of the plant features are useful for a variety of purposes including, for example, generating three-dimensional data models of plants growing in the field, assessing plant growth and phenotypic features, determining what kinds of treatments to apply including both where to apply the treatments and how much, determining whether to remove weeds or other undesirable plants, and so on.

The present disclosure relates to an image processing method and device, a camera component, an electronic device, and a storage medium. The component includes a first camera module sensing first-band light to generate a first image, a second camera module sensing the first-band light and second-band light and generating a second image, an infrared light source emitting the second-band light and a processor. The second image includes a bayer subimage and an infrared subimage. The processor is coupled with the first camera module, the second camera module and the infrared light source respectively. The processor is configured to perform image processing on at least one of the bayer subimage or the infrared subimage and the first image. In the embodiments, a depth image may be acquired without arranging any depth camera in a camera module array, so that the size of the camera component may be reduced, a space occupied by the component in an electronic device may be reduced, and miniaturization and cost reduction of the electronic device are facilitated.

Context-sensitive remote controls for use with electronic devices (e.g., eyewear device). The electronic device is configured to perform activities (e.g., email, painting, navigation, gaming). The context-sensitive remote control includes a display having a display area, a display driver coupled to the display, and a transceiver. The remote control additionally includes memory that stores controller layout configurations for display in the display area of the display by the display driver. A processor in the context-sensitive remote control is configured to establish, via the transceiver, communication with an electronic device, detect an activity currently being performed by the electronic device, select one of the controller layout configurations responsive to the detected activity, and present, via the display driver, the selected controller layout configuration in the display area of the display.