Flash-Mono: 基于前馈加速的高斯点云单目SLAM
Flash-Mono: Feed-Forward Accelerated Gaussian Splatting Monocular SLAM
AI总结：
Flash-Mono通过前馈预测前端和高效隐藏状态模块，解决了单目高斯点云SLAM在时间效率、几何精度和多视角一致性方面的不足，实现10倍速度提升和高质量渲染。
摘要：
单目三维高斯点云SLAM在时间效率、几何精度和多视角一致性方面存在关键限制。这些问题源于训练从零开始的优化过程以及单帧几何先验缺乏帧间尺度一致性。我们提出Flash-Mono系统，包含三个核心模块：前馈预测前端、二维高斯点云映射后端和高效的隐藏状态基于循环闭合模块。我们训练了一个递归前馈前端模型，通过交叉注意机制逐步将多帧视觉特征聚合到隐藏状态中，并联合预测相机姿态和每个像素的高斯属性。通过直接预测高斯属性，我们的方法避免了基于优化的GS-SLAM所需的每帧优化负担，实现了10倍的速度提升，同时保证高质量渲染。我们的递归架构的威力不仅体现在高效的预测上。隐藏状态作为紧凑的子地图描述符，促进了高效的循环闭合和全局Sim(3)优化，以缓解长期存在的漂移问题。为了增强几何保真度，我们用二维高斯surfels替代传统三维高斯椭球体。大量实验表明，Flash-Mono在跟踪和映射质量上均达到最先进水平，突显了其在具身感知和实时重建应用中的潜力。项目页面：https://victkk.github.io/flash-mono.
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
