Abstract: A device and computer implemented method for training a model, in particular a neural network for determining a shape of an object. The method includes determining a first point cloud representation of the object which includes points that represent a first view of the object, determining a second point cloud representation of the object including points that represent a second view of the object, determining a first voxel representation of the object depending on the first point cloud representation which includes voxels that represent the first view, mapping the first voxel representation with the model to a voxel representation of the shape, providing a ground truth for training the model depending on the first and second point cloud representations or depending on the first and a second voxel representation, the second voxel representation being determined depending on the second point cloud representation.

Abstract: A method for processing measurement data which are present as a point cloud of points in space. The point cloud assigns values of one or more measured variables to each point, with regard to a predetermined task. In the method: for each measured variable, all values of the measured variable that are assigned to points of the point cloud are collected and processed to form an aggregated representation. The representation has the same dimensionality irrespective of how many points of the point cloud are assigned values of the relevant measured variable. One or more of these representations are fed as inputs to a task network. The one or more representations are mapped by the task network to the required output with regard to the predetermined task.

Abstract: To address the needs of applications that work with large-scale unstructured point clouds and other noisy data (e.g. image and video data), tangent convolution of 3D data represents 3D data as tangent planes. Tangent convolution estimates tangent planes for each 3D data point in one or more channels of 3D data. Tangent convolution further computes the tangent image signals for the estimated tangent planes. Tangent convolution precomputes the tangent planes and tangent image signals to enable convolution to be performed with greater efficiency and better performance than can be achieved with other 3D data representations.

Abstract: To address the needs of applications that work with large-scale unstructured point clouds and other noisy data (e.g. image and video data), tangent convolution of 3D data represents 3D data as tangent planes. Tangent convolution estimates tangent planes for each 3D data point in one or more channels of 3D data. Tangent convolution further computes the tangent image signals for the estimated tangent planes. Tangent convolution precomputes the tangent planes and tangent image signals to enable convolution to be performed with greater efficiency and better performance than can be achieved with other 3D data representations.