# Flash-Mono: Feed-Forward Accelerated Gaussian Splatting Monocular SLAM arXiv: https://arxiv.org/abs/2604.03092 ยท HTML: https://mlsanity.com/paper/2604.03092/ ## Key facts - Cited by 2 papers in the map (data through 19 Oct 2026). - Placed in SLAM and driving scene reconstruction, one of 15 directions in 3D reconstruction and neural rendering. - Builds on 31 of its 36 references that are in the map. - First posted on arXiv 3 Apr 2026. ## Placed in - 3D reconstruction and neural rendering / [SLAM and driving scene reconstruction](https://mlsanity.com/recon3d/slam-and-driving-scene-reconstruction/) ## Builds on (most cited first, 30 of 31) - [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023) - [ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes](https://mlsanity.com/paper/1702.04405/) (2017) - [Vision Transformers for Dense Prediction](https://mlsanity.com/paper/2103.13413/) (2021) - [DUSt3R: Geometric 3D Vision Made Easy](https://mlsanity.com/paper/2312.14132/) (2023) - [VGGT: Visual Geometry Grounded Transformer](https://mlsanity.com/paper/2503.11651/) (2025) - [2D Gaussian Splatting for Geometrically Accurate Radiance Fields](https://mlsanity.com/paper/2403.17888/) (2024) - [ORB-SLAM: a Versatile and Accurate Monocular SLAM System](https://mlsanity.com/paper/1502.00956/) (2015) - [ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAM](https://mlsanity.com/paper/2007.11898/) (2020) - [DROID-SLAM: Deep Visual SLAM for Monocular, Stereo, and RGB-D Cameras](https://mlsanity.com/paper/2108.10869/) (2021) - [Continuous 3D Perception Model with Persistent State](https://mlsanity.com/paper/2501.12387/) (2025) - [Gaussian Splatting SLAM](https://mlsanity.com/paper/2312.06741/) (2023) - [Fast3R: Towards 3D Reconstruction of 1000+ Images in One Forward Pass](https://mlsanity.com/paper/2501.13928/) (2025) - [MASt3R-SLAM: Real-Time Dense SLAM with 3D Reconstruction Priors](https://mlsanity.com/paper/2412.12392/) (2024) - [Splatt3R: Zero-shot Gaussian Splatting from Uncalibrated Image Pairs](https://mlsanity.com/paper/2408.13912/) (2024) - [FLARE: Feed-forward Geometry, Appearance and Camera Estimation from Uncalibrated Sparse Views](https://mlsanity.com/paper/2502.12138/) (2025) - [UniDepthV2: Universal Monocular Metric Depth Estimation Made Simpler](https://mlsanity.com/paper/2502.20110/) (2025) - [Photo-SLAM: Real-time Simultaneous Localization and Photorealistic Mapping for Monocular, Stereo, and RGB-D Cameras](https://mlsanity.com/paper/2311.16728/) (2023) - [Optimal Transport Aggregation for Visual Place Recognition](https://mlsanity.com/paper/2311.15937/) (2023) - [VGGT-SLAM: Dense RGB SLAM Optimized on the SL(4) Manifold](https://mlsanity.com/paper/2505.12549/) (2025) - [TTT3R: 3D Reconstruction as Test-Time Training](https://mlsanity.com/paper/2509.26645/) (2025) - [Point3R: Streaming 3D Reconstruction with Explicit Spatial Pointer Memory](https://mlsanity.com/paper/2507.02863/) (2025) - [WildGS-SLAM: Monocular Gaussian Splatting SLAM in Dynamic Environments](https://mlsanity.com/paper/2504.03886/) (2025) - [The Revisiting Problem in Simultaneous Localization and Mapping: A Survey on Visual Loop Closure Detection](https://mlsanity.com/paper/2204.12831/) (2022) - [Outdoor Monocular SLAM with Global Scale-Consistent 3D Gaussian Pointmaps](https://mlsanity.com/paper/2507.03737/) (2025) - [DROID-Splat: Combining end-to-end SLAM with 3D Gaussian Splatting](https://mlsanity.com/paper/2411.17660/) (2024) - [VINGS-Mono: Visual-Inertial Gaussian Splatting Monocular SLAM in Large Scenes](https://mlsanity.com/paper/2501.08286/) (2025) - [SEGS-SLAM: Structure-enhanced 3D Gaussian Splatting SLAM with Appearance Embedding](https://mlsanity.com/paper/2501.05242/) (2025) - [GS-LTS: 3D Gaussian Splatting-Based Adaptive Modeling for Long-Term Service Robots](https://mlsanity.com/paper/2503.17733/) (2025) - [A General Framework for Lifelong Localization and Mapping in Changing Environment](https://mlsanity.com/paper/2111.10946/) (2021) - [Dy3DGS-SLAM: Monocular 3D Gaussian Splatting SLAM for Dynamic Environments](https://mlsanity.com/paper/2506.05965/) (2025) ## Built on by (most cited first, 2 of 2) - [GeoGS-SLAM: Geometry-Only Gaussian Splatting for Dense Monocular SLAM](https://mlsanity.com/paper/2607.07452/) (2026) - [Adaptive World Memory 3D Foundation Model for Scalable 3D Mapping, Localization, and Rendering](https://mlsanity.com/paper/2609.21502/) (2026) ## Abstract (arXiv) 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 textitTrain-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 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 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.