Bundle Adjustment (BA) is a fundamental technique in computer vision and photogrammetry used to refine the 3D reconstruction of scenes from multiple images. However, the accuracy of BA is often compromised when dealing with multi-camera setups due to synchronization lags among cameras. This thesis proposal aims to investigate the incorporation of temporal displacement information into bundle adjustment algorithms to compensate for synchronization lags, ultimately improving the accuracy of 3D reconstructions in multi-camera systems.
The primary objectives of this research are as follows:
To develop an innovative bundle adjustment framework that incorporates temporal displacement information to mitigate the impact of synchronization lags in multi-camera systems.
To evaluate the performance of the proposed approach in improving the accuracy of 3D reconstruction when dealing with synchronization lags among cameras.
To optimize the integration of temporal displacement data into bundle adjustment algorithms while considering computational efficiency.
To assess the applicability of the enhanced BA framework in real-world scenarios, such as robotics, autonomous navigation, and multi-camera surveillance systems.
A comprehensive review of existing literature will be conducted to establish the current state of knowledge regarding bundle adjustment, camera synchronization, and the incorporation of temporal information into these techniques. This section will cover relevant technological advancements, case studies, and gaps in existing research related to addressing synchronization lags using temporal displacement in BA.
Temporal Displacement Model Development: Create a mathematical model to represent the temporal displacement among cameras in a multi-camera system.
Integration with Bundle Adjustment: Modify existing bundle adjustment algorithms to incorporate the temporal displacement model as additional constraints, effectively addressing synchronization lags.
Dataset Acquisition: Collect or curate datasets containing synchronized multi-camera sequences with known temporal displacement.
Experimental Evaluation: Conduct experiments to compare the performance of the proposed BA framework with traditional BA methods in scenarios with synchronization lags, focusing on accuracy and computational efficiency.
Optimization and Fine-Tuning: Refine and optimize the integration of temporal displacement information to achieve optimal results while considering real-time applications.
This research is expected to yield the following results:
Development of an innovative bundle adjustment framework that effectively incorporates temporal displacement information to mitigate the impact of synchronization lags in multi-camera systems.
Empirical evidence demonstrating the enhanced accuracy of the proposed approach compared to traditional BA methods in scenarios with synchronization lags.
Recommendations for optimizing and fine-tuning the integration of temporal displacement data into bundle adjustment algorithms, ensuring compatibility with real-time applications.
The incorporation of temporal displacement information into bundle adjustment has significant implications for various fields, including robotics, computer vision, and surveillance systems, where precise 3D reconstructions are essential. This research addresses a critical challenge in multi-camera setups by improving the accuracy of 3D reconstructions despite synchronization lags among cameras.
The research will be conducted over a period of 6m, with the following approximate timeline:
This thesis proposal outlines a comprehensive research plan to develop a bundle adjustment framework that incorporates temporal displacement information to address synchronization lags in multi-camera systems. The research aims to provide an innovative solution that enhances the accuracy of 3D reconstructions in real-world applications, ultimately contributing to advancements in computer vision and robotics.