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.

翻译：近年来，3D高斯溅射（3DGS）技术的进展已能实现基于多视角图像的高质量实时新视角合成。然而，现有方法大多假设物体以单一静态姿态被采集，导致重建结果不完整，遗漏了被遮挡或自遮挡的区域。本文提出PFGS，一种姿态感知的3DGS框架，旨在解决从多姿态图像采集数据中重建完整物体的实际挑战。给定物体在一个主姿态和若干辅助姿态下的图像，PFGS通过迭代方式将每个辅助姿态集融合至主姿态的统一3DGS表示中。我们的姿态感知融合策略结合全局与局部配准，有效合并多视角信息并优化3DGS模型。尽管当前三维基础模型的发展提升了配准的鲁棒性与效率，但其仍受限于高内存需求与次优的精度。PFGS通过更智能地将这些模型整合至配准流程来克服上述局限：利用背景特征进行单姿态相机位姿估计，并借助基础模型实现跨姿态配准。该设计在解决背景不一致性问题的同时，融合了两种方法的优势。实验结果表明，PFGS在定性与定量评估中均稳定优于现有基线方法，能够生成更完整的重建结果与更高保真度的3DGS模型。