公开(公告)号：US20240257410A1

公开(公告)日：2024-08-01

申请号：US18429533

申请日：2024-02-01

Applicant: Cognata Ltd.

Inventor： Dan ATSMON ,  Guy GOLDNER ,  Ilan TSAFRIR

IPC: G06T11/00 ,  B60W50/00 ,  G01S17/89

CPC classification number: G06T11/001 ,  B60W50/00 ,  G01S17/89

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.

公开(公告)号：US20230316789A1

公开(公告)日：2023-10-05

申请号：US18022556

申请日：2021-09-14

Applicant: Cognata Ltd.

Inventor： Ilan TSAFRIR ,  Guy TSAFRIR ,  Ehud SPIEGEL ,  Dan ATSMON

IPC: G06V20/70 ,  G06V20/58 ,  G06V10/77

CPC classification number: G06V20/70 ,  G06V20/58 ,  G06V10/7715

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).