GSplatLoc: Ultra-Precise Camera Localization via 3D Gaussian Splatting
This provides ultra-precise localization for robotics and augmented reality applications, though it appears incremental as it builds on existing 3D Gaussian splatting techniques.
The paper tackles camera localization by using 3D Gaussian splatting for gradient-based pose optimization, achieving translational errors within 0.01 cm and near-zero rotational errors on the Replica dataset.
We present GSplatLoc, a camera localization method that leverages the differentiable rendering capabilities of 3D Gaussian splatting for ultra-precise pose estimation. By formulating pose estimation as a gradient-based optimization problem that minimizes discrepancies between rendered depth maps from a pre-existing 3D Gaussian scene and observed depth images, GSplatLoc achieves translational errors within 0.01 cm and near-zero rotational errors on the Replica dataset - significantly outperforming existing methods. Evaluations on the Replica and TUM RGB-D datasets demonstrate the method's robustness in challenging indoor environments with complex camera motions. GSplatLoc sets a new benchmark for localization in dense mapping, with important implications for applications requiring accurate real-time localization, such as robotics and augmented reality.