Monocular 3D Gaussian Splatting SLAM suffers from critical limitations in time efficiency, geometric accuracy, and multi-view consistency. These issues stem from the time-consuming $\textit{Train-from-Scratch}$ optimization and the lack of inter-frame scale consistency from single-frame geometry priors. We contend that a feed-forward paradigm, leveraging multi-frame context to predict Gaussian attributes directly, is crucial for addressing these challenges. We present Flash-Mono, a system composed of three core modules: a feed-forward prediction frontend, a 2D Gaussian Splatting mapping backend, and an efficient hidden-state-based loop closure module. We trained a recurrent feed-forward frontend model that progressively aggregates multi-frame visual features into a hidden state via cross attention and jointly predicts camera poses and per-pixel Gaussian properties. By directly predicting Gaussian attributes, our method bypasses the burdensome per-frame optimization required in optimization-based GS-SLAM, achieving a $\textbf{10x}$ speedup while ensuring high-quality rendering. The power of our recurrent architecture extends beyond efficient prediction. The hidden states act as compact submap descriptors, facilitating efficient loop closure and global $\mathrm{Sim}(3)$ optimization to mitigate the long-standing challenge of drift. For enhanced geometric fidelity, we replace conventional 3D Gaussian ellipsoids with 2D Gaussian surfels. Extensive experiments demonstrate that Flash-Mono achieves state-of-the-art performance in both tracking and mapping quality, highlighting its potential for embodied perception and real-time reconstruction applications. Project page: https://victkk.github.io/flash-mono.

翻译：单目3D高斯溅射SLAM在时间效率、几何精度和多视图一致性方面存在关键局限。这些问题源于耗时的"从头训练"优化流程，以及基于单帧几何先验导致的帧间尺度不一致。本文认为，利用多帧上下文直接预测高斯属性的前馈范式是解决上述挑战的关键。我们提出Flash-Mono系统，由三个核心模块构成：前馈预测前端、2D高斯溅射建图后端，以及基于隐状态的高效回环检测模块。我们训练了循环前馈前端模型，通过交叉注意力机制逐步将多帧视觉特征聚合为隐状态，并联合预测相机位姿与逐像素高斯属性。通过直接预测高斯属性，本方法跳过了基于优化型GS-SLAM中繁琐的逐帧优化过程，在实现高质量渲染的同时获得$\textbf{10倍}$加速。循环架构的威力不仅体现于高效预测：隐状态作为紧凑的子图描述符，可高效实现回环检测与全局$\mathrm{Sim}(3)$优化，有效缓解长期存在的漂移难题。为提升几何保真度，我们将传统3D高斯椭球体替换为2D高斯面元。大量实验表明，Flash-Mono在跟踪与建图质量上均达当前最优水平，彰显了其在具身感知与实时重建应用中的潜力。项目页面：https://victkk.github.io/flash-mono。