PFGS: Pose-Fused 3D Gaussian Splatting for Complete Multi-Pose Object Reconstruction

• URL: http://arxiv.org/abs/2510.15386v1
• Date: Fri, 17 Oct 2025 07:36:59 GMT
• Title: PFGS: Pose-Fused 3D Gaussian Splatting for Complete Multi-Pose Object Reconstruction
• Authors: Ting-Yu Yen, Yu-Sheng Chiu, Shih-Hsuan Hung, Peter Wonka, Hung-Kuo Chu,
• Abstract summary: PFGS is a pose-aware 3DGS framework that addresses the practical challenge of reconstructing complete objects from multi-pose image captures.<n>Our pose-aware fusion strategy combines global and local registration to merge views effectively.<n> PFGS consistently outperforms strong baselines in both qualitative and quantitative evaluations.
• Score: 43.4603038841558
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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