Abstract: A system for generating synthetic data, comprising at least one processing circuitry adapted for: computing a sequence of partial simulation images, where each of the sequence of partial simulation images is associated with an estimated simulation time and with part of a simulated environment at the respective estimated simulation time thereof; computing at least one simulated point-cloud, each simulating a point-cloud captured in a capture interval by a sensor operated in a scanning pattern from an environment equivalent to a simulated environment, by applying to each partial simulation image of the sequence of partial simulation images a capture mask computed according to the scanning pattern and a relation between the capture interval and an estimated simulation time associated with the partial simulation image; and providing the at least one simulated point-cloud to a training engine to train a perception system comprising the sensor.

Abstract: There is provided a method for annotating digital images for training a machine learning model, comprising: generating, from digital images and a plurality of dense depth maps, each associated with one of the digital images, an aligned three-dimensional stacked scene representation of a scene, where the digital images are captured by sensor(s) at the scene, and where each point in the three-dimensional stacked scene is associated with a stability score indicative of a likelihood the point is associated with a static object of the scene, removing from the three-dimensional stacked scene unstable points to produce a static three-dimensional stacked scene, detecting in at least one of the digital images static object(s) according to the static three-dimensional stacked scene, and classifying and annotating the static object(s). The machine learning model may be trained on the images annotated with a ground truth of the static object(s).

Abstract: A method for an unsupervised analysis of data according to a reordered distance matrix. According to preferred embodiments thereof, the present invention is useful for large scale multidimensional data, more preferably data having at least four dimensions. The present invention is also preferably used for data comprising a plurality of objects characterized by continuous variables, for example variables having a continuum of possible values rather than a plurality of discrete values.