3D Object Tracking via Photometric Bundle Adjustment
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Solution Overview
Problem
Existing methods for 3D reconstruction and tracking of multiple rigid objects moving relative to each other from camera images face challenges in accuracy and robustness, particularly in scenarios with ambiguous object clustering and limited recognition of objects with similar movements, due to uncertainties in optical flow and sparse keypoint density.
Innovation Solution
A direct 3D image alignment and photometric multi-object bundle adjustment method that selectively optimizes parameters using sparse pixel contributions, avoiding regularization terms and leveraging soft or hard assignments to improve object clustering and trajectory estimation, while allowing for online detection and tracking of moving objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If indirect methods (Structure From Motion) are used for 3D reconstruction, then the process can be divided into preprocessing and optimization stages, but the accuracy and robustness are reduced due to uncertainties in optical flow calculation
Solution Approach 1:
The patent inverts the conventional indirect SFM approach by directly calculating 3D structure and camera motion from image intensities and gradients without intermediate optical flow computation. This direct photometric bundle adjustment eliminates the error propagation from optical flow while maintaining process structure through systematic optimization of structural and motion parameters.
2Adaptability or versatility
If dense optical flow is calculated in advance for multi-object reconstruction, then motion segmentation can be performed, but the computational cost and time consumption increase significantly
Solution Approach 1:
The patent extracts only the essential photometric information (image intensities and gradients) needed for motion segmentation and 3D reconstruction, eliminating the need for comprehensive dense optical flow calculation. This selective extraction maintains motion segmentation capability while significantly reducing computational burden through direct photometric error minimization.
3Productivity
If sparse optical flow with keypoint correspondences is used, then the computational load is reduced, but the density of keypoints limits the quality of object clustering and recognition
Solution Approach 1:
The patent replaces the mechanical keypoint correspondence system with a photometric field-based approach. Instead of relying on discrete keypoint matches, the method uses continuous image intensity and gradient information across the entire image, maintaining processing speed while dramatically improving object clustering quality through dense photometric constraints.
4Stability of the object's composition
If regularization terms are applied in bundle adjustment, then the optimization is stabilized, but the accuracy of parameter estimation is reduced due to biased constraints
Solution Approach 1:
The patent converts the potential harm of unstable optimization into benefit by using photometric error minimization with image intensities and gradients as natural constraints. This approach provides optimization stability through the inherent structure of photometric relationships without introducing biased regularization terms, thereby maintaining parameter estimation accuracy.
Data Source
AI summary
The disclosure relates to a method and a system for the detection, 3D reconstruction and tracking of multiple rigid objects moving relative to one another from a series of images from at least one camera and can be used, in particular, in the context of a camera-based environment detection system for assisted or automated driving.