TokenSplat: Token-aligned 3D Gaussian Splatting for Feed-forward Pose-free Reconstruction
This addresses the challenge of 3D scene understanding without known camera poses, which is incremental by building on existing pose-free reconstruction techniques.
The paper tackles the problem of joint 3D Gaussian reconstruction and camera pose estimation from unposed multi-view images, achieving higher reconstruction fidelity and novel-view synthesis quality in pose-free settings, with significant improvements in pose estimation accuracy compared to prior methods.
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/.