Unleashing the Power of Data Synthesis in Visual Localization (2412.00138v1)

Abstract: Visual localization, which estimates a camera's pose within a known scene, is a long-standing challenge in vision and robotics. Recent end-to-end methods that directly regress camera poses from query images have gained attention for fast inference. However, existing methods often struggle to generalize to unseen views. In this work, we aim to unleash the power of data synthesis to promote the generalizability of pose regression. Specifically, we lift real 2D images into 3D Gaussian Splats with varying appearance and deblurring abilities, which are then used as a data engine to synthesize more posed images. To fully leverage the synthetic data, we build a two-branch joint training pipeline, with an adversarial discriminator to bridge the syn-to-real gap. Experiments on established benchmarks show that our method outperforms state-of-the-art end-to-end approaches, reducing translation and rotation errors by 50% and 21.6% on indoor datasets, and 35.56% and 38.7% on outdoor datasets. We also validate the effectiveness of our method in dynamic driving scenarios under varying weather conditions. Notably, as data synthesis scales up, our method exhibits a growing ability to interpolate and extrapolate training data for localizing unseen views. Project Page: https://ai4ce.github.io/RAP/

• The paper introduces RAP, a training framework that significantly reduces translation and rotation errors in visual localization.
• It employs 3D Gaussian splatting to generate synthetic images, thereby diversifying training data and enhancing model generalization.
• Empirical results show up to 50% reduction in translation errors and improved performance under dynamic, real-world conditions.

Unleashing the Power of Data Synthesis in Visual Localization

The paper entitled "Unleashing the Power of Data Synthesis in Visual Localization" explores a novel approach to improving the generalizability of pose regression in visual localization through extensive data synthesis. Visual localization is a critical task that involves estimating a camera's 6-DoF pose within a known environment, and it is crucial in various applications including autonomous vehicles, robotics, and virtual reality. Traditional methods often face challenges when dealing with unseen views, leading to poor performance in practical scenarios. The authors propose a robust training framework, RAP (Robust Absolute Pose regression), that leverages synthetic data to address these limitations.

Key Contributions

1. 3D Gaussian Splatting for Data Synthesis: The paper employs 3D Gaussian Splats (3DGS) to generate synthetic images of varying appearances, which serve to diversify the training data. This approach allows the model to better handle variations in appearance, providing a more robust feature space without relying solely on real-world data. The 3DGS technique efficiently renders new views with novel poses, providing the necessary diversity for effective model training.
2. Two-Branch Joint Training Pipeline: The researchers implement a two-part training approach. The first branch trains a Transformer-based Absolute Pose Regression (APR) model along with both real and synthetic data, employing a discriminator to bridge the syn-to-real domain gap. The second branch enhances the APR model by introducing random perturbations in poses and appearances, further improving generalization.
3. Empirical Validation: Extensive experiments demonstrate that the RAP framework substantially reduces translation and rotation errors compared to state-of-the-art APR methods, achieving better performance without relying exclusively on real data. Notably, the method shows improved capabilities in dynamic and varying weather conditions in driving scenarios, emphasizing its practical applicability in real-world conditions.

Numerical Results

The results are significant: the proposed method achieves a reduction in translation and rotation errors by 50% and 21.6% on indoor datasets, and 35.56% and 38.7% on outdoor datasets, respectively. These improvements are further validated through experiments in dynamic driving scenarios, confirming the method's robustness under diverse environmental conditions.

Implications and Future Directions

The work has profound implications for the practical deployment of AI systems in real-world environments. By significantly enhancing the generalizability of APR models, the RAP framework enables more reliable visual localization, which is crucial for applications requiring precise navigation and spatial awareness. The use of synthetic data generated through 3DLS opens up exciting possibilities for reducing dependency on extensive real-world datasets, which are often expensive and time-consuming to collect.

The paper also sets the stage for future research in AI that could focus on leveraging other forms of synthetic data generation, such as more complex environmental models or real-time adaptation techniques. Integrating geometric priors or temporal data could further enhance the capabilities of pose regression models, pushing the boundaries of what is possible in visual localization tasks.

Overall, the authors of this paper present a comprehensive and effective approach to improving pose regression using synthetic data, with convincing results that could guide future advancements in AI-based localization technologies.