Disclosed is a method for generating high resolution point cloud data for electro-anatomical mapping comprising receiving sparsely measured point cloud data having a plurality of data points. Surface mesh data comprising mesh points defining triangles on a myocardial surface is generated. The point cloud data is mapped to the surface mesh data. For each point of the surface mesh data that cannot be mapped to the point cloud data because there is a missing data point in point cloud data, an interpolation operation is performed based on the point cloud data within the neighbourhood of the point to generate a value for the missing data point. The interpolation operation is repeated N times. For every repetition, a difference between the value for the missing data point generated from the current iteration and the value for the missing data point generated from the immediately preceding iteration is compared, until the difference is below a threshold.

The present disclosure provides an image processing method and apparatus, an electronic device, and a computer-readable storage medium. The method includes: obtaining a first three-dimensional image of a target object in a three-dimensional coordinate system; determining a target plane of the target object in the first three-dimensional image, the target plane comprising target three-dimensional points; projecting the target three-dimensional points to a two-dimensional coordinate system defined on the target plane, to obtain target two-dimensional points; determining a target polygon and a minimum circumscribed target graphic of the target polygon according to the target two-dimensional points; and recognizing the minimum circumscribed target graphic as a first target graphic of the target object in the first target three-dimensional image.

The disclosure is directed to a method for controlling a vehicle in a depot. The method includes the steps: allocating a three-dimensional target object to the vehicle; detecting a three-dimensional object in the environment around the vehicle and determining depth information for the detected three-dimensional object; classifying the detected three-dimensional object on the basis of the determined depth information and checking whether the determined three-dimensional object has the same object class as the three-dimensional target object; identifying the detected three-dimensional object if the determined three-dimensional object has the same object class as the three-dimensional target object by detecting an object identifier assigned to the three-dimensional object and checking whether the detected object identifier matches a target identifier assigned to the target object; outputting an approach signal to move the vehicle closer to the detected three-dimensional target object in an automated manner or manually if the object identifier matches the target identifier.

A method and an apparatus for obtaining 3D information of a vehicle are provided. The method includes: first determining a body boundary line of a first vehicle, and then determining an observation angle and/or an orientation angle of the first vehicle based on the body boundary line.

A system for collecting data for training a computer vision model for shape estimation includes: an imaging system configured to capture one or more images; and a processing system including a processor and memory storing instructions that, when executed by the processor, cause the processor to: receive one or more input images from the imaging system; estimate a pose of an object depicted in the one or more images; render a shape estimate from a 3-D model of the object posed in accordance with the pose of the object; and generate a data point of a training dataset, the data point including one or more images based on the one or more input images and a label corresponding to the one or more images, the label including the shape estimate.

A device configured to capture a first image of an item on a platform using a camera and to determine a first number of pixels in the first image that corresponds with the item. The device is further configured to capture a first depth image of an item on the platform using a three-dimensional (3D) sensor and to determine a second number of pixels within the first depth image that corresponds with the item. The device is further configured to determine that the difference between the first number of pixels in the first image and the second number of pixels in the first depth image is less than the difference threshold value, to extract the plurality of pixels corresponding with the item in the first image from the first image to generate a second image, and to output the second image.

An information processing device includes processing circuitry. The processing circuitry obtain target information that indicates at least one of a distance to a target object or a position of the target object. The processing circuitry generate, based on the target information, map information of a space including a plurality of areas, the map information indicating presence or absence of the target object in a first area included in the plurality of areas, and a detailed position of the target object in the first area.

Jitter is removed from point cloud data of a target by fitting the data to 3-D models of possible targets. The point cloud data is de-jittered as a group by shifting the point cloud data in its coordinate system until a minimum fit error is observed between the shifted data and a 3-D model under analysis. Different 3-D models may be evaluated in succession until a 3-D model is identified that has the least fit error. The 3-D model with the least fit error most likely represents the identity of the target.

A method for environmental acquisition for acquiring the surrounding environment of a vehicle. Using a plurality of cameras and/or sensor devices, from at least two positions, images of the surrounding environment are produced, and on the basis of the images a free surface, as well as a contour bounding the free surface, are ascertained. Separately for each image and the contour determined on the basis of this image, for a multiplicity of points along the contour, feature vectors are calculated and entered into a one-dimensional array. Through matching of the one-dimensional arrays, correspondences are sought for which the three-dimensional position is subsequently ascertained by triangulation. A computing unit for carrying out at least individual method steps of the method is also described.

Systems and methods are provided for enhanced animation generation based on generative modeling. An example method includes training models based on faces and information associated with persons, each face being defined based on location information associated with facial features, and identity information for each person. The modeling system being trained to reconstruct expressions, textures, and models of persons.