TokenSplat：面向无位姿前馈重建的词元对齐三维高斯泼溅
TokenSplat: Token-aligned 3D Gaussian Splatting for Feed-forward Pose-free Reconstruction
把跨视角语义对应直接放在特征空间里做词元对齐，并用非对称双流解码器把相机词元与图像词元解耦，这套设计可迁移到任何需要从无位姿多视图前馈重建三维表示（高斯/NeRF/点云）的流程中，省去逐场景优化与迭代位姿精化。
原文依据 a feed-forward framework for joint 3D Gaussian reconstruction and camera pose estimation from unposed multi-view images
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论本文提出 TokenSplat，一个从无位姿多视图图像中联合进行三维高斯重建与相机姿态估计的前馈框架。其核心是引入词元对齐高斯预测模块，直接在特征空间中跨视图对齐语义对应的信息。该模块以粗粒度词元位置与融合置信度为引导，聚合多尺度上下文特征，从而实现长程跨视图推理，并减少重叠高斯带来的冗余。为进一步增强姿态鲁棒性并将视角线索从场景语义中解耦，TokenSplat 采用可学习相机词元与非对称双流解码器（ADF-Decoder），在相机词元与图像词元之间施加方向性约束的通信。这在前馈架构内保持了清晰的因子分解，无需迭代精化即可实现连贯重建与稳定姿态估计。大量实验表明，TokenSplat 在无位姿设定下取得更高的重建保真度与新视角合成质量，并且相较此前的无位姿方法显著提升了姿态估计精度。
英文原文摘要 1171 字符
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.