看，高斯，无需位姿：无需精确位姿初始化使用Gaussian Splatting进行新视角合成
Look Gauss, No Pose: Novel View Synthesis using Gaussian Splatting without Accurate Pose Initialization
AI总结：
提出一种扩展3D Gaussian Splatting框架的方法，通过优化光度残差的外参相机参数，实现无需精确位姿初始化的快速3D场景重建与新视角合成，在LLFF上达到SOTA且运行时间减少2-4倍。
摘要：
3D Gaussian Splatting最近作为一种强大的工具出现，用于从一组已定位的输入图像中快速且准确地进行新视角合成。然而，像大多数新视角合成方法一样，它依赖于准确的相机位姿信息，这限制了其在获取准确相机位姿可能具有挑战性甚至不可能的现实场景中的适用性。我们提出了一种对3D Gaussian Splatting框架的扩展，通过基于光度残差优化外参相机参数。我们推导了解析梯度，并将其计算与现有的高性能CUDA实现相集成。这使得下游任务成为可能，例如6-DoF相机位姿估计以及联合重建和相机优化。特别是，我们在真实场景的位姿估计上实现了快速收敛和高精度。我们的方法通过联合优化几何和相机位姿，实现了3D场景的快速重建而无需准确的位姿信息，同时在新视角合成中取得了最先进的结果。我们的方法在优化上比大多数竞争方法快得多，在渲染上快几倍。我们在真实场景和通过模拟环境的复杂轨迹上展示了结果，在LLFF上取得了最先进的结果，同时与最有效的竞争方法相比将运行时间减少了2到4倍。源代码将在https://github.com/Schmiddo/noposegs上提供。
英文摘要：
3D Gaussian Splatting has recently emerged as a powerful tool for fast and accurate novel-view synthesis from a set of posed input images. However, like most novel-view synthesis approaches, it relies on accurate camera pose information, limiting its applicability in real-world scenarios where acquiring accurate camera poses can be challenging or even impossible. We propose an extension to the 3D Gaussian Splatting framework by optimizing the extrinsic camera parameters with respect to photometric residuals. We derive the analytical gradients and integrate their computation with the existing high-performance CUDA implementation. This enables downstream tasks such as 6-DoF camera pose estimation as well as joint reconstruction and camera refinement. In particular, we achieve rapid convergence and high accuracy for pose estimation on real-world scenes. Our method enables fast reconstruction of 3D scenes without requiring accurate pose information by jointly optimizing geometry and camera poses, while achieving state-of-the-art results in novel-view synthesis. Our approach is considerably faster to optimize than most competing methods, and several times faster in rendering. We show results on real-world scenes and complex trajectories through simulated environments, achieving state-of-the-art results on LLFF while reducing runtime by two to four times compared to the most efficient competing method. Source code will be available at https://github.com/Schmiddo/noposegs .
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
