A method and processor system are provided which analyze a depth map, which may be obtained from a range sensor capturing depth information of a scene, to identify where an object is located in the scene. Accordingly, a region of interest may be identified in the scene which includes the object, and image data may be selectively obtained of the region of interest, rather than of the entire scene containing the object. This image data may be acquired by an image sensor configured for capturing visible light information of the scene. By only selectively obtaining the image data within the region of interest, rather than all of the image data, improvements may be realized in the computational complexity of a possible further processing of the image data, the storage of the image data and/or the transmission of the image data.

The present disclosure discloses a map display method performed at a terminal. The method includes: obtaining a real scene image of a current location and target navigation data of navigation from the current location to a destination; determining, according to the current location and the target navigation data, virtual navigation prompt information to be overlaid in the real scene image; determining, according to a device configuration parameter of a target device capturing the real scene image, a first location on which the virtual navigation prompt information is overlaid in the real scene image; performing verification detection on the current device configuration parameter; and overlaying the virtual navigation prompt information on the first location when the current device configuration parameter passes through the verification detection. According to the present disclosure, an AR technology is applied to the navigation field so that map display manners are more diverse and diversified.

The present disclosure discloses a map display method performed at a terminal. The method includes: obtaining a real scene image of a current location and target navigation data of navigation from the current location to a destination; determining, according to the current location and the target navigation data, virtual navigation prompt information to be overlaid in the real scene image; determining, according to a device configuration parameter of a target device capturing the real scene image, a first location on which the virtual navigation prompt information is overlaid in the real scene image; performing verification detection on the current device configuration parameter; and overlaying the virtual navigation prompt information on the first location when the current device configuration parameter passes through the verification detection. According to the present disclosure, an AR technology is applied to the navigation field so that map display manners are more diverse and diversified.

A system and method for determining the measurements needed to fabricate prescription eyeglasses. A person is scanned with a time-of-flight scanner while wearing the eyeglass frames. This produces depth maps from known distances. Common measurement points are identified within at least some of the scans. The positional changes of the common measurement points and the known distance to the imaging camera are utilized to map three dimensional coordinates for the measurement points using an actual measurement scale. Fabrication measurements are calculated between the various three-dimensional coordinates in the same scale.

A system and method for determining the measurements needed to fabricate prescription eyeglasses. A person is scanned with a time-of-flight scanner while wearing the eyeglass frames. This produces depth maps from known distances. Common measurement points are identified within at least some of the scans. The positional changes of the common measurement points and the known distance to the imaging camera are utilized to map three dimensional coordinates for the measurement points using an actual measurement scale. Fabrication measurements are calculated between the various three-dimensional coordinates in the same scale.

Methods, systems, and apparatus, including computer programs encoded on a storage device, for electric grid asset detection are enclosed. An electric grid asset detection method includes: obtaining overhead imagery of a geographic region that includes electric grid wires; identifying the electric grid wires within the overhead imagery; and generating a polyline graph of the identified electric grid wires. The method includes replacing curves in polylines within the polyline graph with a series of fixed lines and endpoints; identifying, based on characteristics of the fixed lines and endpoints, a location of a utility pole that supports the electric grid wires; detecting an electric grid asset from street level imagery at the location of the utility pole; and generating a representation of the electric grid asset for use in a model of the electric grid.

Methods, systems, and apparatus, including computer programs encoded on a storage device, for electric grid asset detection are enclosed. An electric grid asset detection method includes: obtaining overhead imagery of a geographic region that includes electric grid wires; identifying the electric grid wires within the overhead imagery; and generating a polyline graph of the identified electric grid wires. The method includes replacing curves in polylines within the polyline graph with a series of fixed lines and endpoints; identifying, based on characteristics of the fixed lines and endpoints, a location of a utility pole that supports the electric grid wires; detecting an electric grid asset from street level imagery at the location of the utility pole; and generating a representation of the electric grid asset for use in a model of the electric grid.

A system includes a computing device. The computing device is configured to perform a set of functions. The set of functions includes receiving an image, wherein the image comprises a two-dimensional array of data. The set of functions includes extracting, by a two-dimensional neural network, a plurality of two-dimensional features from the two-dimensional array of data. The set of functions includes generating a linear combination of the plurality of two-dimensional features to form a single three-dimensional input feature. The set of functions includes extracting, by a three-dimensional neural network, a plurality of three-dimensional features from the single three-dimensional input feature. The set of functions includes determining a two-dimensional depth map. The two-dimensional depth map contains depth information corresponding to the plurality of three-dimensional features.

A system includes a computing device. The computing device is configured to perform a set of functions. The set of functions includes receiving an image, wherein the image comprises a two-dimensional array of data. The set of functions includes extracting, by a two-dimensional neural network, a plurality of two-dimensional features from the two-dimensional array of data. The set of functions includes generating a linear combination of the plurality of two-dimensional features to form a single three-dimensional input feature. The set of functions includes extracting, by a three-dimensional neural network, a plurality of three-dimensional features from the single three-dimensional input feature. The set of functions includes determining a two-dimensional depth map. The two-dimensional depth map contains depth information corresponding to the plurality of three-dimensional features.

An information processing device (200A, 200B, and 200C) according to the present disclosure includes a control unit (220, 220B, and 220C). The control unit (220, 220B, and 220C) acquires a captured image of a target imaged by a sensor. The captured image is an image obtained from reflected light of light emitted to the target from a plurality of light sources arranged at different positions, respectively. The control unit (220, 220B, and 220C) extracts a flat region from the captured image based on a luminance value of the captured image. The control unit (220, 220B, and 220C) calculates shape information regarding a shape of a surface of the target based on information regarding the sensor and the flat region of the captured image.