3D Pose Estimation From Uncalibrated Multi-View Images
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Solution Overview
Problem
Existing 3D pose estimation techniques rely on multi-camera calibration, which is resource-intensive and difficult to implement with cameras having changing zoom levels or moving during image capture, especially in dynamic environments like sporting events.
Innovation Solution
A neural network regression analysis is performed to generate 3D graphical models from uncalibrated image capture devices, estimating 3D poses and device parameters without prior knowledge of camera parameters, using feed-forward neural networks and optimization techniques to account for multiple views.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multi-camera calibration is used for 3D pose estimation, then measurement precision is improved, but device complexity and ease of operation deteriorate due to resource-intensive calibration requirements
Solution Approach 1:
The patent extracts and removes the calibration step from the 3D pose estimation system. Instead of requiring multi-camera calibration, the system uses a single image capture device and eliminates the calibration apparatus entirely, thereby reducing device complexity while maintaining pose estimation functionality
Solution Approach 2:
The system performs self-calibration by using the subject themselves as the calibration reference. The subject wears a garment with identifiable features that serve as both the measurement target and the calibration standard, eliminating the need for external calibration equipment or procedures
2Manufacturing precision
If multi-camera calibration is performed, then manufacturing precision is improved, but ease of manufacture deteriorates due to difficulty in implementing with moving or zooming cameras
Solution Approach 1:
The patent makes the system dynamic by allowing the image capture device to move, pan, tilt, and zoom during image capture without requiring calibration. The garment on the subject adapts to different camera positions and angles, enabling the system to handle dynamic shooting conditions that were previously incompatible with calibration-based methods
Solution Approach 2:
The calibration requirement is extracted and removed from the system, eliminating the constraint that prevented easy implementation with moving or zooming cameras. This allows the system to be manufactured and deployed in dynamic environments like sporting events without complex calibration infrastructure
3Ease of operation
If uncalibrated image capture devices are used, then ease of operation is improved, but measurement precision deteriorates without prior knowledge of camera parameters
Solution Approach 1:
The system uses the subject's garment as a self-contained calibration reference. The garment includes features with known dimensions and patterns that enable the processing circuitry to automatically determine camera parameters and perform accurate 3D pose estimation without external calibration equipment or operator intervention
Solution Approach 2:
The system changes the approach from requiring known camera parameters to deriving parameters from the image data itself. By analyzing the garment features in the captured image, the system calculates the necessary geometric parameters to achieve accurate 3D pose estimation from a single uncalibrated device
4Measurement precision
If traditional 3D pose estimation is implemented, then productivity is reduced due to time-consuming calibration processes, but measurement precision is maintained
Solution Approach 1:
The time-consuming calibration process is extracted and removed from the workflow. The system jumps directly to pose estimation using the subject's garment as a built-in reference, enabling real-time tracking and significantly improving productivity for applications like sporting event analysis
Solution Approach 2:
The garment is prepared in advance with identifiable features that serve as pre-configured calibration markers. This preliminary preparation of the garment eliminates the need for on-site calibration procedures, allowing immediate real-time pose estimation when the subject is captured
Data Source
AI summary
Apparatus and methods for three-dimensional pose estimation are disclosed herein. An example apparatus includes an image synchronizer to synchronize a first image generated by a first image capture device and a second image generated by a second image capture device, the first image and the second image including a subject; a two-dimensional pose detector to predict first positions of keypoints of the subject based on the first image and by executing a first neural network model to generate first two-dimensional data and predict second positions of the keypoints based on the second image and by executing the first neural network model to generate second two-dimensional data; and a three-dimensional pose calculator to generate a three-dimensional graphical model representing a pose of the subject in the first image and the second image based on the first two-dimensional data, the second two-dimensional data, and by executing a second neural network model.