GloSplat: Joint Pose-Appearance Optimization for Faster and More Accurate 3D Reconstruction

• URL: http://arxiv.org/abs/2603.04847v1
• Date: Thu, 05 Mar 2026 06:02:50 GMT
• Title: GloSplat: Joint Pose-Appearance Optimization for Faster and More Accurate 3D Reconstruction
• Authors: Tianyu Xiong, Rui Li, Linjie Li, Jiaqi Yang,
• Abstract summary: We present GloSplat, a framework that performs emphjoint pose-appearance optimization during 3D Gaussian Splatting training.<n>Unlike prior joint optimization methods, GloSplat preserves emphexplicit SfM feature tracks as first-class entities throughout training.<n>Experiments demonstrate that GloSplat-F achieves state-of-the-art among COLMAP-free methods while GloSplat-A surpasses all COLMAP-based baselines.
• Score: 35.30036388020098
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Feature extraction, matching, structure from motion (SfM), and novel view synthesis (NVS) have traditionally been treated as separate problems with independent optimization objectives. We present GloSplat, a framework that performs \emph{joint pose-appearance optimization} during 3D Gaussian Splatting training. Unlike prior joint optimization methods (BARF, NeRF--, 3RGS) that rely purely on photometric gradients for pose refinement, GloSplat preserves \emph{explicit SfM feature tracks} as first-class entities throughout training: track 3D points are maintained as separate optimizable parameters from Gaussian primitives, providing persistent geometric anchors via a reprojection loss that operates alongside photometric supervision. This architectural choice prevents early-stage pose drift while enabling fine-grained refinement -- a capability absent in photometric-only approaches. We introduce two pipeline variants: (1) \textbf{GloSplat-F}, a COLMAP-free variant using retrieval-based pair selection for efficient reconstruction, and (2) \textbf{GloSplat-A}, an exhaustive matching variant for maximum quality. Both employ global SfM initialization followed by joint photometric-geometric optimization during 3DGS training. Experiments demonstrate that GloSplat-F achieves state-of-the-art among COLMAP-free methods while GloSplat-A surpasses all COLMAP-based baselines.

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