Object Model Rotation Using Direct Orthogonal Matrix Prediction
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Solution Overview
Problem
Existing neural network models struggle with poor performance in rotation estimation due to the need for Gram-Schmidt (GS) processing to convert a 3×2 matrix to a 3×3 rotation matrix, leading to issues like gradient backflow and numerical gradient errors, which hinder accurate rotation of object models.
Innovation Solution
The method involves obtaining an n-order matrix from input information, performing unbiased Gram-Schmidt orthogonalization to convert it into an n-order rotation matrix, and directly using this matrix to rotate the n-dimensional model, bypassing traditional orthogonalization processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional Gram-Schmidt orthogonalization processing is performed to convert a 3×2 matrix to a 3×3 rotation matrix, then the rotation matrix can be obtained for model rotation, but the model training performance deteriorates due to gradient backflow and numerical gradient errors
Solution Approach 1:
Instead of converting a 3×2 matrix to a 3×3 rotation matrix through traditional Gram-Schmidt orthogonalization, the patent inverts the approach by directly predicting a 3×3 rotation matrix from the input data. This inversion eliminates the need for post-processing orthogonalization and its associated numerical instability issues, directly improving both rotation estimation accuracy and model training performance
2Ease of operation
If Gram-Schmidt orthogonalization processing is used to obtain a rotation matrix, then rotation of the object model can be completed, but the processing complexity and computational overhead increase
Solution Approach 1:
The patent extracts and removes the problematic Gram-Schmidt orthogonalization processing step from the rotation estimation pipeline. By directly predicting the rotation matrix without this intermediate conversion step, the method simplifies the overall process while maintaining the essential functionality of obtaining a valid rotation matrix for model rotation
Solution Approach 2:
The patent performs preliminary action by directly predicting a valid 3×3 rotation matrix structure from the input data, ensuring the matrix is already in the correct format and properties before it is needed for model rotation. This eliminates the need for subsequent orthogonalization processing and reduces computational complexity
Data Source
AI summary
This disclosure discloses an object model rotation method and a related device. The method includes: After information about a target object in a first state is obtained, the information may be input into the target model. Next, the information may be processed by using the target model, to obtain a first matrix, where the first matrix is an n-order matrix, and n is a positive integer greater than or equal to 2. Then, the first matrix may be orthogonalized to obtain a second matrix, where the second matrix is an n-order rotation matrix. Finally, a preset n-dimensional model of the target object may be rotated directly based on the second matrix, to obtain a rotated n-dimensional model of the target object.