用3D高斯点云实现精准视觉定位，尤其适合纹理缺失场景。
GSLoc: Visual Localization with 3D Gaussian Splatting
- 以3D高斯点云为地图，通过反向传播优化相机位姿。
- 在纹理缺失且重叠度低的条件下，定位成功率优于现有神经稀疏方法。
- 利用渲染生成虚拟关键帧，提升图像检索效果，适合复杂环境定位。
我们提出GSLoc：一种基于3D高斯点云（3DGS）作为场景地图表示的新视觉定位方法。GSLoc通过渲染管线反向传播位姿梯度，对齐渲染图像与目标图像，并采用粗到精策略，利用模糊核缓解问题非凸性，提升收敛性。实验表明，在初始帧与目标帧重叠度较低、且环境缺乏纹理的挑战性条件下，该方法仍能成功完成定位，而现有先进神经稀疏方法表现较差。利用3DGS地图生成的真实感渲染作为副产品，我们进一步提出在图像检索中融合观测与虚拟参考关键帧的方法，显著提升定位性能。我们在合成数据和真实世界数据上进行了评估，讨论了该方法的优势与应用潜力。
原文摘要 · Abstract (English)
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.
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