FR-CDNet: Unified Scene Change Detection Model across Viewpoint Variations and Different Temporal Conditions

Abstract

Scene Change Detection (SCD) is a critical task for building smart cities, yet its practical application faces dual challenges: existing methods typically rely on temporal conditions present in the training data and the ideal assumption of small viewpoint differences. Consequently, they struggle to handle the common and significant viewpoint variations in real-world scenarios and exhibit strong sensitivity to temporal conditions, leading to drastic performance degradation under unseen temporal settings. To address these challenges, we propose the Fusion-Refinement Change Detection Network (FR-CDNet). By modeling correspondences between objects and preserving spatial prior information from ideally aligned scenes during the disentangled processing of different temporal directions, our network achieves a unified handling of varying degrees of viewpoint variations and different temporal conditions---a capability existing methods lack. Furthermore, FR-CDNet can automatically distinguish the temporal attribution of change entities to better support downstream tasks. To better evaluate performance in real-world settings, we further construct the URSCD dataset, which includes larger viewpoint differences and more diverse change scenarios. Extensive experiments demonstrate the universal scene detection capability of our method: it achieves significant improvement in F1-score on unaligned scenes while maintaining performance comparable to SOTA on aligned scenes. Ablation studies further demonstrate that the proposed framework can be migrated to enhance various mainstream models, effectively eliminating temporal condition dependency while improving overall performance.