Multi-scene camera relocalization via modulated coordinate regression and low-rank adaptation

Camera relocalization, the task of estimating a camera’s 6-DoF pose from a single image, typically necessitates training a separate model for each scene or performing fine-tuning to adapt to new environments. In this work, we present a novel approach for multi-scene camera relocalization using a single, unified regressor. Our method builds upon a pre-trained encoder, trained on a diverse set of scenes, to extract generalizable features. To adapt this encoder for specific scenes, we employ parameter-efficient fine-tuning. Additionally, we introduce a lightweight feature modulation mechanism that incorporates compact scene embeddings to condition the model, allowing it to distinguish between scenes without requiring dedicated branches or retraining. Experiments on standard relocalization benchmarks demonstrate that our method achieves competitive accuracy across multiple scenes compared to scene-specific models, while significantly reducing model complexity and training parameters. Notably, our model utilizes over 5 times fewer trainable parameters and over 3 times fewer deployment parameters than recent multi-scene counterparts, while delivering superior performance. The proposed framework provides a scalable and generalizable solution for camera relocalization in real-world, multi-environment applications.