Dense Bundle Adjustment

Dense Bundle Adjustment (DBA) refines 3D scene reconstructions by jointly optimizing camera poses and a dense 3D model, aiming for highly accurate and detailed maps. Recent research emphasizes integrating DBA with deep learning, particularly using neural implicit representations for efficient 3D model parameterization and leveraging deep optical flow for improved geometric consistency. This fusion of geometric and learning-based approaches, often within a factor graph framework for multi-sensor integration, enables robust and real-time dense mapping in large-scale environments, with applications in autonomous driving and other robotics tasks. The resulting improvements in accuracy and scalability are driving significant advancements in 3D scene understanding and localization.