用于新视角合成（NVS）的对应引导式无SfM三维高斯泼溅方法
Correspondence-Guided SfM-Free 3D Gaussian Splatting for NVS
AI总结：
针对无SfM的新视角合成中初始位姿不准导致的优化不稳定、收敛差问题，提出对应引导式三维高斯泼溅方法，通过像素对应优化位姿与场景，性能和效率均优于现有最优基准。
摘要：
无需运动恢复结构（Structure-from-Motion，SfM）预处理相机位姿的新视角合成（Novel View Synthesis，NVS）——即无SfM方法——对于提升快速响应能力、增强对多变运行条件的鲁棒性至关重要。近期的无SfM方法整合了位姿优化，设计出端到端框架以联合估计相机位姿并完成NVS。但现有多数工作依赖逐像素图像损失函数，例如L2损失。在无SfM方法中，不准确的初始位姿会导致错位问题，在逐像素图像损失函数的约束下，这会产生过量梯度，引发优化不稳定且NVS收敛效果差。本研究提出一种用于NVS的对应引导式无SfM三维高斯泼溅方法。我们利用目标图像与渲染结果之间的对应关系实现更优的像素对齐，促进帧间相对位姿的优化，随后将学习到的位姿用于优化整个场景。每个二维屏幕空间像素通过近似表面渲染与其对应的三维高斯相关联，以助力梯度反向传播。实验结果表明，与当前最优基准方法相比，所提方法具备更优异的性能与时间效率。
英文摘要：
Novel View Synthesis (NVS) without Structure-from-Motion (SfM) pre-processed camera poses--referred to as SfM-free methods--is crucial for promoting rapid response capabilities and enhancing robustness against variable operating conditions. Recent SfM-free methods have integrated pose optimization, designing end-to-end frameworks for joint camera pose estimation and NVS. However, most existing works rely on per-pixel image loss functions, such as L2 loss. In SfM-free methods, inaccurate initial poses lead to misalignment issue, which, under the constraints of per-pixel image loss functions, results in excessive gradients, causing unstable optimization and poor convergence for NVS. In this study, we propose a correspondence-guided SfM-free 3D Gaussian splatting for NVS. We use correspondences between the target and the rendered result to achieve better pixel alignment, facilitating the optimization of relative poses between frames. We then apply the learned poses to optimize the entire scene. Each 2D screen-space pixel is associated with its corresponding 3D Gaussians through approximated surface rendering to facilitate gradient back propagation. Experimental results underline the superior performance and time efficiency of the proposed approach compared to the state-of-the-art baselines.
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。
