EDeRF: Updating Local Scenes and Editing Across Fields for Real-Time Dynamic Reconstruction of Road Scene

NeRF provides high reconstruction accuracy but is slow for dynamic scenes. Editable NeRF speeds up dynamics by editing static scenes, reducing retraining and succeeding in autonomous driving simulation. However, the lack of depth cameras and the difficulty in obtaining precise vehicle poses make real-time dynamic road scene reconstruction challenging, particularly in swiftly and accurately reconstructing new vehicles entering the scene and their trajectories. We propose EDeRF, a method for real-time dynamic road scene reconstruction from fixed cameras such as traffic surveillance through collaboration of sub-NeRFs and cross-field editing. We decompose the scene space and select key areas to update new vehicles by sharing parameters and local training with sub-fields. These vehicles are then integrated into the complete scene and achieve dynamic motion by warping the sampling rays across different fields, where vehicles’ six degrees of freedom(6-DOF) is estimated based on inter-frame displacement and rigid body contact constraints. We have conducted physical experiments simulating traffic monitoring scenes. Results show that EDeRF outperforms comparative methods in efficiency and accuracy in reconstructing the appearance and movement of newly entered vehicles.