The system includes a metric map creation unit configured to create a metric map using first image data received from a 3D sensor, an image processing unit configured to recognize an object by creating and classifying a point cloud using second image data received from an RGB camera; a probability-based map production unit configured to create an object location map and a spatial semantic map in a probabilistic expression method using a processing result of the image processing unit, a question creation unit configured to extract a portion of high uncertainty about an object class from a produced map on the basis of entropy and ask a user about the portion, and a map update unit configured to receive a response from the user and update a probability distribution for spatial information according to a change in probability distribution for classification of the object.

Embodiments are directed to neural network processing for multi-object three-dimensional (3D) modeling. An embodiment of a computer-readable storage medium includes executable computer program instructions for obtaining data from multiple cameras, the data including multiple images, and generating a 3D model for 3D imaging based at least in part on the data from the cameras, wherein generating the 3D model includes one or more of performing processing with a first neural network to determine temporal direction based at least in part on motion of one or more objects identified in an image of the multiple images or performing processing with a second neural network to determine semantic content information for an image of the multiple images.

A method for monitoring the environment of a vehicle includes evaluating physical measurement data obtained from the environment of the vehicle to determine whether at least one person is approaching the vehicle, how many people approach the vehicle may also be recorded. The method includes evaluating physical measurement data obtained from the environment of the vehicle to determine whether at least one person is moving away from the vehicle and, if appropriate, the number of people that are moving away from the vehicle is also recorded. The method further includes carrying out a check as to whether the number of people that have moved away from the vehicle corresponds to the number of people that have previously approached the vehicle. In response to the check resulting in a difference, it is determined that the vehicle is in an unsafe state.

A method for monitoring the environment of a vehicle includes evaluating physical measurement data obtained from the environment of the vehicle to determine whether at least one person is approaching the vehicle, how many people approach the vehicle may also be recorded. The method includes evaluating physical measurement data obtained from the environment of the vehicle to determine whether at least one person is moving away from the vehicle and, if appropriate, the number of people that are moving away from the vehicle is also recorded. The method further includes carrying out a check as to whether the number of people that have moved away from the vehicle corresponds to the number of people that have previously approached the vehicle. In response to the check resulting in a difference, it is determined that the vehicle is in an unsafe state.

The present invention relates to a method for deriving a 3D data from image data comprising: receiving, from at least one camera, image data representing an environment; detecting, from the image data, at least one object within the environment; classifying the at least one detected object, wherein the method comprises, for each classified object of the classified at least one objects: determining a 2D skeleton of the classified object by implementing a neural network to identify features of the classified object in the image data corresponding to the classified object; and constructing a 3D skeleton for the classified object, comprising mapping the determined 2D skeleton to 3D.

A bulk sorting system for sorting objects in bulk is provided. The bulk sorting system includes: at least one radiation source arranged to radiate the objects, at least one optical sensor arranged to capture reflected radiation of the objects and acquire the reflected radiation as multi- or hyperspectral data; a processing circuit configured to analyze the reflected radiation of the objects by inputting the multi- or hyperspectral data into a convolutional neural network (CNN) with at least two convolutional layers in order to either detect and classify the objects in the multi- or hyperspectral data and/or semantically segment the multi- or hyperspectral data; and a mechanical sorter configured to sort the objects according to their classification and/or segmentation using the analysis of the processing circuit such that different overlapping and/or stacked objects are separated or treated as a single group of objects.

The present disclosure provides an under-screen optical fingerprint identification apparatus and a fingerprint identification method. The under-screen optical fingerprint identification apparatus comprises: a photodetection array which has a photosensitive area on which a photosensitive pixel array is provided, wherein the photosensitive pixel array comprises at least three types of pixels; and a fingerprint contact identification area located above the photodetection array and comprising a light-emitting layer and a cover layer, wherein the cover layer has a contact area, and the light-emitting layer has an identification area corresponding to the contact area, wherein the identification area comprises light-emitting units, each of which comprises a light-transmitting area and at least three light-emitting areas located therearound for emitting detection light of corresponding wavebands, wherein the detection light is reflected by an object to be identified to form signal light which passes through the filter layers and reaches corresponding three types of pixels.

A point cloud data processing apparatus 11 includes a processor configured to acquire first form information that indicates a feature of a form of a first object, specify an object region of a second object that is identified from an image and that corresponds to the first form information, select second-object point cloud data, in point cloud data, that corresponds to the object region, on the basis of the object region, acquire second form information that indicates a feature of a form of the second object, on the basis of the second-object point cloud data, and compare the first form information with the second form information and perform determination as to whether the second object is the first object.

A measurement system includes a processor. Based on information measured by a camera that takes an image of a measurement target and an auxiliary object arranged on the measurement target, the processor acquires first point cloud data representing a three-dimensional geometry of the measurement target including the auxiliary object. Based on the first point cloud data and second point cloud data that is known and that represents a three-dimensional geometry of the measurement target, the processor eliminates point cloud data of the auxiliary object from the first point cloud data. The processor compares the first point cloud data, from which the point cloud data of the auxiliary object has been eliminated, with the second point cloud data. The processor displays information relating to a result of comparison on a display device.

Systems and methods are described for creating three dimensional models of building objects by creating a point cloud from a plurality of input images, defining edges of the building object's surfaces represented by the point cloud, creating simplified geometries of the building object's surfaces and constructing a building model based on the simplified geometries. Input images may include ground, orthographic, or oblique images. The resultant model may be scaled according to correlation with select image types and textured.