iComMa：通过比较与匹配反演3D Gaussian Splatting以进行相机位姿估计
iComMa: Inverting 3D Gaussian Splatting for Camera Pose Estimation via Comparing and Matching
AI总结：
提出 iComMa 方法，通过反演 3D Gaussian Splatting 构建无需训练的可微框架，结合端到端匹配模块与像素级比较损失，有效解决了无 CAD 模型场景下大旋转和平移等不良初始化的 6D 相机位姿估计难题。
摘要：
我们提出了一种名为 iComMa 的方法，以解决计算机视觉中的 6D 相机位姿估计问题。传统的位姿估计方法通常依赖于目标的 CAD 模型，或者需要针对特定物体类别进行专门的网络训练。一些现有方法通过反演神经辐射场（NeRF），在无网格物体和场景的位姿估计中取得了有前景的结果。然而，它们在处理诸如大旋转和平移等不良初始化时仍然存在困难。为了解决这个问题，我们提出了一种通过反演 3D Gaussian Splatting (3DGS) 来进行准确相机位姿估计的高效方法。具体而言，一个基于梯度的可微框架通过最小化查询图像与渲染图像之间的残差来优化相机位姿，且无需训练。设计了一个端到端的匹配模块来增强模型对不良初始化的鲁棒性，同时最小化像素级比较损失有助于实现精确的位姿估计。在合成和复杂真实世界数据上的实验结果证明了所提方法在挑战性条件下的有效性以及相机位姿估计的准确性。
英文摘要：
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.
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