PFGS：用于完整多姿态物体重建的位姿融合三维高斯泼溅
PFGS: Pose-Fused 3D Gaussian Splatting for Complete Multi-Pose Object Reconstruction
AI总结：
PFGS提出一种位姿感知的三维高斯泼溅框架，通过全局与局部配准融合多姿态图像，利用基础模型和背景特征解决遮挡与背景不一致问题，实现完整且高保真的物体重建。
摘要：
三维高斯泼溅（3DGS）的最新进展使得从多视角图像进行高质量、实时的全新视角合成成为可能。然而，大多数现有方法假设物体以单一静态姿态被捕获，导致重建不完整，遗漏了被遮挡或自遮挡的区域。我们提出了PFGS，一种位姿感知的3DGS框架，以解决从多姿态图像捕获中重建完整物体的实际挑战。给定一个物体在一个主姿态和若干辅助姿态下的图像，PFGS迭代地将每个辅助姿态集合融合到主姿态的统一3DGS表示中。我们的位姿感知融合策略结合了全局和局部配准，以有效合并视图并细化3DGS模型。尽管三维基础模型的最新进展提高了配准的鲁棒性和效率，但它们仍受限于高内存需求和次优的准确性。PFGS通过更智能地将这些模型整合到配准过程中来克服这些挑战：它利用背景特征进行每姿态相机位姿估计，并采用基础模型进行跨姿态配准。这种设计兼顾了两者的优点，同时解决了背景不一致的问题。实验结果表明，PFGS在定性和定量评估中均持续优于强基线，产生更完整的重建和更高保真度的3DGS模型。
英文摘要：
Recent advances in 3D Gaussian Splatting (3DGS) have enabled high-quality, real-time novel-view synthesis from multi-view images. However, most existing methods assume the object is captured in a single, static pose, resulting in incomplete reconstructions that miss occluded or self-occluded regions. We introduce PFGS, a pose-aware 3DGS framework that addresses the practical challenge of reconstructing complete objects from multi-pose image captures. Given images of an object in one main pose and several auxiliary poses, PFGS iteratively fuses each auxiliary set into a unified 3DGS representation of the main pose. Our pose-aware fusion strategy combines global and local registration to merge views effectively and refine the 3DGS model. While recent advances in 3D foundation models have improved registration robustness and efficiency, they remain limited by high memory demands and suboptimal accuracy. PFGS overcomes these challenges by incorporating them more intelligently into the registration process: it leverages background features for per-pose camera pose estimation and employs foundation models for cross-pose registration. This design captures the best of both approaches while resolving background inconsistency issues. Experimental results demonstrate that PFGS consistently outperforms strong baselines in both qualitative and quantitative evaluations, producing more complete reconstructions and higher-fidelity 3DGS models.
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。
