  We present a method named iComMa to address the 6D camera pose estimation
problem in computer vision. Conventional pose estimation methods typically rely
on the target's CAD model or necessitate specific network training tailored to
particular object classes. Some existing methods have achieved promising
results in mesh-free object and scene pose estimation by inverting the Neural
Radiance Fields (NeRF). However, they still struggle with adverse
initializations such as large rotations and translations. To address this
issue, we propose an efficient method for accurate camera pose estimation by
inverting 3D Gaussian Splatting (3DGS). Specifically, a gradient-based
differentiable framework optimizes camera pose by minimizing the residual
between the query image and the rendered image, requiring no training. An
end-to-end matching module is designed to enhance the model's robustness
against adverse initializations, while minimizing pixel-level comparing loss
aids in precise pose estimation. Experimental results on synthetic and complex
real-world data demonstrate the effectiveness of the proposed approach in
challenging conditions and the accuracy of camera pose estimation.
