3D Pose Correction Engine for Object Authentication from Varied Poses
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Solution Overview
Problem
The proliferation of counterfeit items in the market, particularly limited release items, leads to a lack of trust in transactions and hampers the growth of various industries, as users often cannot verify the authenticity of purchased items.
Innovation Solution
A Pose Correction Engine that processes user source images to determine authenticity by generating pose corrected images, aligning them with predefined poses using a segmentation, depth estimation, and registration phase, and utilizes machine learning to enhance accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the system requires users to capture source images in perfect alignment with predefined poses, then the measurement precision for authentication is improved, but the ease of operation deteriorates
Solution Approach 1:
The system performs pose correction as a preliminary processing step before authentication. The pose correction engine automatically adjusts captured images to match predefined poses, eliminating the need for users to manually achieve perfect alignment during image capture. This preliminary correction maintains measurement precision while significantly improving ease of operation.
Solution Approach 2:
The pose correction engine acts as an intermediary between the user-captured image and the authentication system. It transforms images with varying poses into a standardized format that the authentication algorithm can process, bridging the gap between user convenience and system precision requirements.
2Reliability
If the system uses pose correction processing, then the reliability of authentication is improved, but the loss of time in processing increases
Solution Approach 1:
Pose correction is performed as a preliminary step before authentication to ensure reliable results. By correcting poses upfront, the system prevents authentication failures that would require re-processing, ultimately reducing total processing time while maintaining high reliability.
Solution Approach 2:
The system adjusts pose parameters (rotation angles, translation offsets) to align images with predefined poses. These parameter transformations are computationally efficient and can be performed quickly, minimizing time loss while significantly improving authentication reliability through consistent pose alignment.
Data Source
AI summary
Various embodiments are directed to a Pose Correction Engine (“Engine”). The Engine generates a reference image of the object of interest. The reference image portrays the object of interest oriented according to a first pose. The Engine receives a source image of an instance of the object. The source image portrays the instance of the object oriented according to a variation of the first pose. The Engine determines a difference between the first pose of the reference image and the variation of the first pose of the source image. The Engine identifies, based on the determined difference, one or portions of a three-dimensional (3D) map of a shape of the object obscured by the variation of the first pose portrayed in the source image. The Engine generates a pose corrected image of the instance of the object that portrays at least a portion of the source image and at least the identified portion of the 3D map of the shape of the object.