Bundle Adjustment

Bundle adjustment (BA) is a fundamental optimization technique used in 3D reconstruction and simultaneous localization and mapping (SLAM) to refine camera poses and 3D scene geometry by minimizing the discrepancies between observed and predicted features. Current research emphasizes improving BA's efficiency and robustness, particularly for large-scale datasets and challenging scenarios like non-Lambertian surfaces, rolling shutter cameras, and dynamic environments, often employing advanced optimization algorithms (e.g., second-order methods, variable projection) and integrating deep learning for feature extraction and improved convergence. These advancements are crucial for applications ranging from robotics and augmented reality to photogrammetry and remote sensing, enabling more accurate and efficient 3D scene understanding.