GS-CPR: Efficient Camera Pose Refinement via 3D Gaussian Splatting (2408.11085v4)

Abstract: We leverage 3D Gaussian Splatting (3DGS) as a scene representation and propose a novel test-time camera pose refinement (CPR) framework, GS-CPR. This framework enhances the localization accuracy of state-of-the-art absolute pose regression and scene coordinate regression methods. The 3DGS model renders high-quality synthetic images and depth maps to facilitate the establishment of 2D-3D correspondences. GS-CPR obviates the need for training feature extractors or descriptors by operating directly on RGB images, utilizing the 3D foundation model, MASt3R, for precise 2D matching. To improve the robustness of our model in challenging outdoor environments, we incorporate an exposure-adaptive module within the 3DGS framework. Consequently, GS-CPR enables efficient one-shot pose refinement given a single RGB query and a coarse initial pose estimation. Our proposed approach surpasses leading NeRF-based optimization methods in both accuracy and runtime across indoor and outdoor visual localization benchmarks, achieving new state-of-the-art accuracy on two indoor datasets. The project page is available at https://xrim-lab.github.io/GS-CPR/.

• The paper introduces GSLoc, a test-time framework that refines camera poses via 3D Gaussian Splatting to significantly enhance localization accuracy.
• It employs direct RGB image matching with an exposure-adaptive module to overcome challenges from variable lighting conditions without relying on pre-trained features.
• Experimental results on standard benchmarks demonstrate that GSLoc outperforms NeRF-based methods, achieving high accuracy and real-time efficiency for practical applications.

An Academic Overview of "GSLoc: Efficient Camera Pose Refinement via 3D Gaussian Splatting"

In the paper "GSLoc: Efficient Camera Pose Refinement via 3D Gaussian Splatting," the authors propose a novel framework aimed at refining camera pose estimation with high efficiency and accuracy. This work leverages the 3D Gaussian Splatting (3DGS) technique, capitalizing on its ability to render high-quality novel views swiftly. The approach introduces significant advancements over traditional methods by integrating this rendering capability with advanced pose refinement strategies directly using RGB images—a method which addresses various challenges inherent in real-world visual localization tasks.

Key Contributions and Approach

The principal contribution of the paper is GSLoc, a test-time camera pose refinement framework that enhances the localization accuracy in the domain of Absolute Pose Regression (APR) and Scene Coordinate Regression (SCR). The method employs 3D Gaussian Splatting for scene representation, which facilitates rendering high-quality synthetic images and depth maps needed to establish precise 2D-3D correspondences.

A standout feature of GSLoc is its elimination of the need for pre-trained feature extractors or descriptors—common in existing methods—by directly operating on RGB images in conjunction with MASt3R, a 3D vision foundation model. This approach provides accurate 2D matching without additional training requirements, making the method both efficient and robust to various photometric conditions encountered in practical scenarios.

Moreover, to improve robustness in outdoor environments where lighting conditions may vary significantly between mapping and query image capture, the GSLoc framework incorporates an exposure-adaptive module. This component adjusts the exposure of rendered views to match that of the query image, further enhancing matching accuracy.

Experimental Results

The evaluation of GSLoc across standard benchmarks (7Scenes, 12Scenes, and Cambridge Landmarks) reveals notable performance improvements over both state-of-the-art NeRF-based methods and traditional APR/SCR approaches. GSLoc sets new standards in localization accuracy on indoor datasets and displays impressive resilience and adaptability in outdoor scenarios. Importantly, it achieves these improvements with a runtime efficiency that renders it suitable for real-time applications, significantly outpacing NeRF-based alternatives that rely on iterative optimization processes.

Implications and Potential Future Work

The ability of GSLoc to provide state-of-the-art pose refinement with considerably reduced computational overhead has practical implications in fields such as robotics, augmented reality (AR), and autonomous navigation. By reducing dependence on extensive feature descriptor databases and complex iterative optimization, GSLoc paves the way for more deployable and scalable localization technologies.

Future work could explore further integration of 3D Gaussian Splatting with other neural representation methodologies to extend its applicability and efficiency. Additionally, enhancing the framework to handle more complex dynamic scenes and extending the approach to incorporate multimodal data (such as inertial measurements) may yield further gains in accuracy and robustness.

In conclusion, GSLoc represents a meaningful advancement in the field of camera pose estimation, coupling the nuanced benefits of novel view rendering with direct RGB matching to achieve a refined and efficient localization methodology. Its alignment with ongoing trends in 3D representation and neural network reliance highlights its potential impact on the future development of visual localization technologies.