We present GSLoc: a new visual localization method that performs dense camera alignment using 3D Gaussian Splatting as a map representation of the scene. GSLoc backpropagates pose gradients over the rendering pipeline to align the rendered and target images, while it adopts a coarse-to-fine strategy by utilizing blurring kernels to mitigate the non-convexity of the problem and improve the convergence. The results show that our approach succeeds at visual localization in challenging conditions of relatively small overlap between initial and target frames inside textureless environments when state-of-the-art neural sparse methods provide inferior results. Using the byproduct of realistic rendering from the 3DGS map representation, we show how to enhance localization results by mixing a set of observed and virtual reference keyframes when solving the image retrieval problem. We evaluate our method both on synthetic and real-world data, discussing its advantages and application potential.
我们提出了GSLoc，一种新的视觉定位方法，该方法使用3D高斯点作为场景的地图表示进行稠密相机对齐。GSLoc通过渲染管道反向传播位姿梯度，来对齐渲染图像与目标图像，同时采用粗到细的策略，利用模糊核来缓解问题的非凸性并提高收敛性。结果表明，在初始帧和目标帧之间重叠较小且环境纹理较少的具有挑战性的条件下，我们的方法在视觉定位上取得了成功，而最新的神经稀疏方法表现较差。利用3DGS地图表示中逼真渲染的副产品，我们展示了如何通过在解决图像检索问题时混合一组观测到的和虚拟的参考关键帧来增强定位结果。我们在合成和真实世界的数据上对该方法进行了评估，并讨论了其优势和应用潜力。