Calibrating 3D Mesh Models for Articulated Object Tracking
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Solution Overview
Problem
Articulated objects, such as the human body or a laptop computer, are challenging to track accurately and efficiently from captured sensor data due to their unique shapes and variations within object classes.
Innovation Solution
A calibration engine computes shape parameters of a 3D mesh model using an optimization process to find the similarity between sensor data and the model, enabling accurate calibration and tracking by minimizing an energy function that measures similarity, even when the function is non-smooth and contains discontinuities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a generic 3D mesh model is used for tracking articulated objects, then the tracking process is simpler and faster, but the accuracy deteriorates due to shape variations between different objects in the same class
Solution Approach 1:
The patent applies parameter changes by computing individualized shape parameters for each object instance. The system represents objects using a parameterized 3D mesh model where shape parameters (such as PCA coefficients) capture the specific geometry of each object. By optimizing these parameters to match the actual object, the system achieves high tracking accuracy without requiring a completely custom model for each object, thus balancing accuracy and complexity.
2Measurement precision
If an optimization process is used to compute shape parameters, then the calibration accuracy improves, but the computational time increases due to the non-smooth and discontinuous nature of the energy function
Solution Approach 1:
The patent applies preliminary action by performing an initialization step before the main optimization process. The system uses an initial guess for shape parameters (potentially from a generic model or rough estimation) to start the optimization. This preliminary setup helps the optimization algorithm converge faster and avoid getting stuck in poor local minima, reducing the overall computational time while maintaining calibration accuracy.
Solution Approach 2:
The patent applies feedback through the iterative optimization process that minimizes an energy function measuring the difference between the rendered model and captured sensor data. The system continuously evaluates the energy function and adjusts shape parameters based on the feedback from this evaluation. This feedback mechanism guides the optimization toward the optimal calibration while allowing for efficient termination when convergence is achieved.
3Measurement precision
If individualized shape parameters are computed for each object, then the pixel classification accuracy improves, but the calibration process becomes more complex
Solution Approach 1:
The patent applies parameter changes by using a compact parameterization of object shapes through Principal Component Analysis (PCA). Instead of storing complete 3D models for each object, the system represents shapes using a small number of PCA coefficients that capture the essential shape variations. This parameter reduction achieves individualized calibration with high pixel classification accuracy while keeping the calibration process manageable through dimensionality reduction.
Data Source
AI summary
Examples describe an apparatus for calibrating a three dimensional (3D) mesh model of an articulated object. The articulated object is an instance of a specified object class. The apparatus comprises an input configured to receive captured sensor data depicting the object. The apparatus has a calibration engine configured to compute values of shape parameters of the 3D mesh model which indicate which member of the object class is depicted in the captured sensor data, in order to calibrate the 3D mesh model. The calibration engine is configured to compute the values of the shape parameters with an optimization process to find at least one potential local or global minimum of an energy function, the energy function expressing a degree of similarity between data rendered from the model and the received sensor data.