Cognex‘s patented method uses dynamic weighting between point-to-plane and point-to-edge metrics to find the pose of a 3D model in an acquired image. By matching edges and normals, the system provides robust alignment for various object geometries, enhancing the accuracy of 3D image data alignment. GlobalData’s report on Cognex gives a 360-degree view of the company including its patenting strategy. Buy the report here.

According to GlobalData’s company profile on Cognex, AI-assisted repair estimation was a key innovation area identified from patents. Cognex's grant share as of January 2024 was 66%. Grant share is based on the ratio of number of grants to total number of patents.

3d model pose estimation using edges and normals

Source: United States Patent and Trademark Office (USPTO). Credit: Cognex Corp

A recently granted patent (Publication Number: US11881000B2) discloses a method for determining the pose of a 3D model in a 3D image captured by a 3D camera assembly. The method involves generating a 3D model during training, acquiring a 3D image of an object during runtime, and utilizing a processor to find a pose that matches 3D edges and 3D normals in the model to those in the image. The 3D model includes representations of 3D edges and 3D surface normals of the object imaged by the camera assembly. Additionally, the method incorporates weightings to prioritize the use of 3D edges versus 3D normals in the image, enabling accurate alignment determination based on the information available.

Furthermore, the patent details a process for performing a coarse 3D alignment by registering identified features in the 3D image with corresponding features of the 3D model. This alignment is based on the comparison of edges or normals in both the image and the model. The method also involves analyzing the identified features in the image to generate weight values, which contribute to the accurate determination of the pose of the 3D model. By utilizing a combination of point-to-line and point-to-plane metrics for matching 3D edges and normals, the method ensures a comprehensive approach to pose estimation, enhancing the overall accuracy and efficiency of the process.

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