TokenSplat: Token-aligned 3D Gaussian Splatting for Feed-forward Pose-free Reconstruction

• URL: http://arxiv.org/abs/2603.00697v1
• Date: Sat, 28 Feb 2026 15:13:13 GMT
• Title: TokenSplat: Token-aligned 3D Gaussian Splatting for Feed-forward Pose-free Reconstruction
• Authors: Yihui Li, Chengxin Lv, Zichen Tang, Hongyu Yang, Di Huang,
• Abstract summary: TokenSplat is a feed-forward framework for joint 3D Gaussian reconstruction and camera pose estimation.<n>At its core, TokenSplat introduces a Token-aligned Gaussian Prediction module.<n>It aggregates multi-scale contextual features to enable long-range cross-view reasoning.
• Score: 45.41545304485825
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present TokenSplat, a feed-forward framework for joint 3D Gaussian reconstruction and camera pose estimation from unposed multi-view images. At its core, TokenSplat introduces a Token-aligned Gaussian Prediction module that aligns semantically corresponding information across views directly in the feature space. Guided by coarse token positions and fusion confidence, it aggregates multi-scale contextual features to enable long-range cross-view reasoning and reduce redundancy from overlapping Gaussians. To further enhance pose robustness and disentangle viewpoint cues from scene semantics, TokenSplat employs learnable camera tokens and an Asymmetric Dual-Flow Decoder (ADF-Decoder) that enforces directionally constrained communication between camera and image tokens. This maintains clean factorization within a feed-forward architecture, enabling coherent reconstruction and stable pose estimation without iterative refinement. Extensive experiments demonstrate that TokenSplat achieves higher reconstruction fidelity and novel-view synthesis quality in pose-free settings, and significantly improves pose estimation accuracy compared to prior pose-free methods. Project page: https://kidleyh.github.io/tokensplat/.

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