Flash-Mono: Feed-Forward Accelerated Gaussian Splatting Monocular SLAM
Authors: Zicheng Zhang
Ke Wu
Xiangting Meng
Keyu Liu
Jieru Zhao
Wenchao Ding
Venue: ICLR 2026 Poster (2026)
Keywords: SLAM
3DGS
3D Reconstruction
3D Foundation Model
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: 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.

FLASH-MONO: FEED-FORWARD ACCELERATED GAUSSIAN SPLATTING MONOCULAR SLAM
Zicheng Zhang', Ke Wu', Xiangting Meng", Keyu Liu Jieru Zhao Wenchao Ding"
Fudan University TShanghaiTech University *Shanghai Jiao Tong University
https://victk:: . thub. ho/ffash-more
Flash-Mono
Fred
MonoCS
Fied Depeh Fird Nermal
Depth
L1
SSIM
DepthCS
S3PO-CS
SeanNet
Ours
INV ATE
INV
LPISS
1.6 DepthCS
Timeline
SMPO-GS
Depth
INV
SSIM
MonoC2
Bundle Fusion
DepthGG
SUPO-GS
Ours
INV ATE
INV LPWL
12.0
4
MonoCS
DeptiiCs
SIROSS
Cuirs
Figure 1: Our Results for Reconstruction and Rendering & Tracking & Speed Metrics.
Our method reconstructs high-quality Gaussian maps in complex scenes with multiple rooms and varying lighting conditions. The right-side radar chart shows our rendering quality (PSNR, SSIM, LPIPS) and trajectory tracking accuracy (ATE), with reciprocals of LPIPS, ATE, and Depth LI plotted for clarity. Our method outperforms others in both rendering quality and trajectory accuracy, offering a 10x speedup over contemporary monocular GS-SI SLAM methods.
ABSTRACT
Monocular Gaussian Splatting SLAM suffers from critical limitations time ef- ficiency, geometric accuracy, and multi-view consistency. These issues stem from the time-consuming Train-from-Senet -Scrtchh optimization and the lack of inter-frame scale consistency from single-frame frame geometry priors. We contend that feed- forward paradigm, leveraging multi-frame context to predict Gaussian attributes directly, is crucial for addressing these challenges. We present Flash-Mono, system composed of three core modules: feed-forward prediction frontend, a 2D Gaussian Splatting mapping backend, and efficient hidden-state-Be based loop closure module. We trained a recurrent feed-forward frontend model that progres- sively aggregates multi-frame visual features into hidden state via attention and jointly predicts camera poses and per-pixel Gaussian properties. By directly predicting Gaussian attributes, our method bypasses the burdensome pea-frame optimization required in optimization-base GS-SLAM, achieving 10x speedup while ensuring high-quality rendering. The power of our recurrent architecture extends beyond efficient prediction. The hidden states act compact submap descriptors, facilitating efficient loop closure and global Sim(3) optimization mitigate the long-stas mingsstnnding challenge of drift. For enhanced geometric fidelity, we replace conventional 3D Gaussian ellipsoids with 2D Gaussian surfels. Extensive
"Corresponiing author.

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experiments demonstrate that Flash-Mono achieves state-of-the-an performance in both tracking and mapping quality, highlighting its potential for embodied per- ception and real-time reconstruction applications.
INTRODUCTION
Recent advancements in real-time 3D scene reconstruction using a single RGB camera have attracted considerable attention. Its ability to provide dense and information-rich maps is crucial for appli- cations ranging from robotic navigation and spatial intelligence. While traditional representations like point clouds, voxels, and surfels have been widely used, 3D Gaussian Splatting (3DGS) (Kerbl itt al., (023) has recently emerged as highly promising approach for 3D reconstruction, owing to its capabilities in differentiable rendering, self-supervised training from RGB images, and high- fidelity novel view synthesis. Consequently, integrating 3DGS into a real-time monocular SLAM framework presents significant opportunity for advancing embodied perception.
An early attempt at monocular GS-SLAM (Matsuki et al. 2024) initializes Gaussians randomly and relies on hundreds of optimization iterations per frame to maintain a consistent map. Subsequent methods (Zheng et al.] 2025; Wu et al. 2025a) employ depth or optical flow prediction networks to provide geometric priors, which are used to initialize Gaussian geometric attributes. However, their performance remains limited to around I FPS, insufficient for real-time SLAM, as they do not abandon the 'yain-from-Scrate paradigm (Gaussian appearance attributes are randomly initialized and trained). Moreover, these approaches also suffer from severe multi-view inconsistencies, as monocular depth predictions are inherently scale-inconsistent. On different front, feed-forward methods such VGGT (Wang et al.) 2025a) have demonstrated excellent multi-frame consistency by applying cross-altention over image batches. While the feed-forward approach supplies a consis- tent geometric prior, its offline requirement of processing all frames once makes it fundamentally incompatible with the streaming input and low-latency pose estimation required by SLAM.
Based on this analysis, we identify three critical challenges that impede the development of a truly real-time and globally consistent monocular GS-SLAM system. First, the prevalent Train-from- Scratch paradigm of Gaussian Splatting requires dozens hundreds of iterations of optimization per keyframe, fundamentally preventing real-time performance. Second, incremental feed-forward reconstruction methods are susceptible to cumulative pose and scale drift, as past predictions cannot be refined by future observations, leading to poor multi-frame geometric consistency. Third, vanilla 3DGS representations often suffer from poor geometry quality.
To overcome these challenges, we propose Flash-Mono, a monocular GS-SLAN system designed to deliver exceptional speed performance and high-quality mapping. At its core is a recurrent feed- forward reconstruction model that incrementally predicts camera poses together with dense, pixel- aligned Gaussian representation for each incoming frame. This design directly addresses the ef- ficiency bottleneck of optimization-based GS-SLAM: instead of training Gaussians from scratch at every keyframe, we predict high-quality Gaussians and only apply lightweight backend refine- ment. To combat the drift that common in incremental feed-forward reconstruction, we leverage the model's ' hidden state as compact submap descriptor: when revisiting a location, a single condi- tional forward pass produces an accurate Sim(3) loop constraint, which we integrate into pose graph optimization for global correction. Finally, to improve geometric fidelity, we adopt 2D Gaussian surfels as our map primitive, providing a stronger surface prior than vanilla 3DGS. With these com- ponents, Flash-Mono supports streaming inputs while achieving real-time performance and globally consistent reconstructions. In summary, our main contributions are:
We propose real-tiim (10 FPS+) monocular GS- GS-M framework that leverages recur- rent forward model to predict poses and Gaussians directly. Compared to all previous methods that require training Gaussians entirely from scratch, our framework achieves markable speed improvements while still ensuring high-quality results.
We design a novel and efficient loop closure method based on the hidden state of the feed- forward model, and through Sim(3) graph optimization, we mitigate accumulated errors while preserving the global consistency of the reconstructed map.
We conduct extensive experiments on large-scale and challenging datasets, evaluating ren- dering, geometry, tracking, and efficiency metrics. Our work achieves tate--tftth-art
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sults in both tracking and rendering quality, while significantly surpassing previous meth- ods in processing speed.
2 RELATED WORKS
SLAM with 3D Foundation Model. Feed-forward architectures have recently emerged as a powerful alternative to classical cectuurefrro--Motion (SfM) pipelines, which rely on iterative feature matching and bundle adjustment (Schönberger & Frahm 2016). Early works such as DUSt3R (Wang et al. (2044) and its extension MAS13R (Murai al. 2025) pioneered this paradigm by directly predicting point maps from image pairs within single forward pass. To overcome the limitation of pairwise inputs, Fast3R (Yang et al. 2025) introduced a transformer-based design capable of processing multiple images in parallel, thereby accelerating large-scale 3D reconstruc- tion. CUT3R (Wang et al. (025b) further advanced this direction by adopting a recurrent frame- work that accommodates a variable number of images and supports diverse input modalities, en- abling online processing of video streams. Extending this line of work, VGGT (Wang et al., (025a) demonstrated the potential of large-scale, multi-task learning for feed-forward reconstruction, while FLARE (Zhang et al. 2025) and Splatt3r (Smart et al., (224)) extended the idea to renderable Gaus- sian Splatting representations directly from unposed images.
Nevertheless, directly applying feed-forward methods to SLAM remains highly challenging due to the need for accurate pose consistency, temporal stability, and long-horize robustness. For exam- ple, although MASt3R-SLAA
(Murai et al.) 2025) partially mitigates some of these issues with improved correspondence strategies, its design not tailored for persistent SLAMM Later, VGGT- SLAM (Maggio et al., (025) builds on the strong backbone of VGGT (Wang et al.) 2025a), feeding submaps into it and optimizing poses on the (4( manifold to achieve more accurate tracking.
Monocular GS-SLAM. 3D Gaussian Splatting (3DGS) (Kerbl et al., 2023) has recently gained attention in monocular SLAM research due to its differentiable nature and real-time rendering ef- ficiency. MonoGS (Matsuki et al. 2024) and PhotoSLAM (Huang et al.) are early monocular GS-SLAM methods that initialize Gaussian ellipsoids through feature points or random sampling and incorporate ORB-SLAME (Mur-Artal et al., 2015) for pose estimation, enabling applications in small indoor environments. SEGS-SLAM (Tianci Wen 2025) further enhances structural con- sistency by modeling appearance variations. DroidSplat (Homeyer al., 2025) leverages dense optical flow and depth priors to achieve robust tracking and reconstruction. However, these monoc- ular systems suffer from scalability issues and often generate floating artifacts in dynamic or large- scale scenes. Building upon these limitations, approaches like WildGS-SLAM (Zheng et al. 2025), DepthGS (Zhao et al.) 2025). and Dy3DGS-SLL SLAM (Li al.) 2025) introduced geometry prior and pixel-level uncertainty estimation to enhance robustness in real wooldd dynamic scenes. Furthermore, S3PO-GS [Cheng al., 2025) addresses the challenges of scale drift and the lack of geometric pri- ors commonly encountered in outdoor scenarios by introducing scale self-consistent pointmap. However, existing GS-based SLAM methods are generally limited to around 1 FPS, which is clearly insufficient to meet the inherent real-time requirements of SLAM. The main reason lies in the fact that these methods train the Gaussians from scratch for each keyframe, typically requiring tens to hundreds of iterations. Since single iteration takes approximately 20 ms, the total training time per keyframe is roughly one second, inevitably resulting in slow overall performance.
3 PRELIMINARIES: 2D GAUSSIAN FOR GEOMETRIC ACCURACY
The original 3D Gaussian Splatting (3DGS) (Kerbl et al. 2023) often produces noisy geometry with "floater" artifacts, as volumetric primitives lack explicit surface constraints. To address this, 2D Gaussian Splatting (2DGS) was introduced in (Huang et al.] 2024). representing scenes as a collection of 2D planar Gaussian surfels. Their work demonstrated that this representation provides stronger geometric priors, yielding significantly improved surface accuracy and multi-view consistency over 3DGS.
We adopt 2DGS as scene representation, where each surfel is defined by its position (μ),, color (c), opacity (o), rotation (r), and 2D scale (s). The final pixel color (f), depth (Đ), and accumulation
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Feed-Forward Network Pretraining
Feedd-Forwwrd Caussian Splatting SLAM
RGB Stream
Loop Closure Module
Stream
submaps
Relative Pose Estimation
Feod-Torrann Network
VII
Bag. of Hidden Stales
I oop Frame Forward
Recurrent Neural Network
SIM(S Pose Grapá Optimization
Ma-1
As
MLP
DPT Head
DPT Head
Gaussian Map
Refine
Voselization
Prume
Pose (Te.s)
Point Cloud (Pt+1)
Gaussian (8:-1)
Loup Correction
Figure 2: Pipeline. For each new frame, our recurrent model jointly infers the camera pose and per- pixel 2DGS attributes conditioned hidden state. The hidden state is updated simultaneously. To avoid catastrophic forgetting, the stream is partitioned into submaps. The hidden state is reinitialized for each submap. Past hidden states are cached in the Bag of Hidden States. Upon loop detection, i.e., revisiting location, we perform a single forward pass on the loop frame conditioned on the past hidden state relocalize the current frame in the past submap. A following pose graph optimization is then performed to correct the full trajectory. In the backend, per- wer-frame 2DGS attributes prediction voxelized, merged, and refined to build global 2DGS map.
(A) are rendered via volumetric alpha blending:
(-1
wp(p) exp
mi)
(1)
(i, A) E (c Rig 1):;
II (1-w,)
Here, p denotes pixel coordinate, E; E 2x2 is the screen-space covariance induced by the surfel's rotation r; and 2D scale 8;, and z; is the surfel depth along the camera optical axis used for depth accumulation. Compared to 3D Gaussian ellipsoids, the planar 2DGS representation provides stronger surface prior that suppresses floaters and improves geometric fidelity, which particularly beneficial for SLAM where small geometrie inconsistencies the remainder of this paper, we use 2DGS as our map primitive: our recurrent feed-forward frontend predicts per-pixel surfel attributes in the current camera frame, and our backend incrementally fuses and refines these predictions into a global, renderable map that can be efficiently updated after pose graph optimization.
quickly accumulate into drift. In
OUR APPROACH
In this section, we introduce our approach in the following order. We first describe our recurrent feed-forward frontend, which constitutes the core of our system by incrementally estimating camera poses and per-frame 2DGS attributes ($4.1). We then present our loop closure mechanism, which leverages the model's hidden state to enable global drift correction via Sim(3) optimization ($4.2). Finally, we detail the backend mapping method that incrementally fuses the frontend's raw predic- tions into a globally consistent 2DGS map
4.1 RECURRENT FEED-FORWARD FRONTEND MODEL.
The input of our system is monocular RGB stream {4.. For each incoming frame I, R mx x3 timestep i, our feed-forward model, denoted by takes the current frame and hidden state M,-1
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imput. The function of model is to jointly predict three outputs: (a) the camera pose T, SE(3), representing the transformation from the current camera frame to the coordinate system of the initial frame (t = 1); (b) dense, pixel-aligned 2DGS map S. {9., ]== where the attributes of each Gaussian surfel are defined in the local coordinate system of the current camera; and (c) an updated hidden state Mr. which carries aggregated information forward to the next timestep (the initial state Mo is initialized zero). Formally, the per-frame prediction process is expressed as:
Te, Gr Mr fl4, M1-1)
Model Architecture. Inspired by Wang et (0025b) and Wu et al. (2025b), we design state- ful transformer architecture to incrementally reconstruct the scene. Each incoming image is first converted into set of visual tokens F; E R C by ViT encoder. The model then employs two interconnected decoders that facilitate bidirectional information exchange between visual tokens F, and the persistent hidden state M,-1 via cross- attentotnnnon. A learnable pose token žt, concatenated with Fr, is processed by the decoders to aggregate geometric cues for pose estimation. This fusion can be expressed as:
Fr = Encoder(4,)
(3)
Fl, 2/, M, = Decoders((Fr =t), M,
(4)
Finally, two DPT heads (Ranftl al. 2021 decode the image tokens Fy to predict 2DGS attributes: the means and confidences (it, C.), and other parameters (ōr, Pr. &,, Concurrently, MLP head extracts the absolute camera pose 7ú from the output pose token 24.
pe; C₁ Headl ( (F)
(5)
0, Pr, S, C Headgs (F))
(6)
T. = Head (z/)
(7)
Training Objective. Our model is trained on large-scale datasets with ground-truth RGB, depth, and camera pose data. The training objective consists of three loss components, summed over sequence length The predicted pose T. is parameterized as a quaternion q and a translation vector 7. The total loss is weighted sum of the pose loss, geometric loss, and rendering loss:
Liotal Apose + Xgcofogo + frenndr
L
Σ
(lle - gell2 + llft - rxl12)
(9)
I. HxW
Lyon
Σ
(Cem [pa, - -m,,ll log(Er,
(10)
m=
Lrendor
Ameell4e iell2 Atpipe (le, Ie)
Adepan DD D.ll2)
(11)
where i, and D, are the RGB and depth rendered from the predicted 2DGS map G. through standard rasterization as described in $3 Here, (qt, 7z), It and D, denote the ground-truth camera pose, RGB image, and depth map, respectively; μ is obtained by unprojecting the ground-truth depth map using the camera intrinsics. Our model is trained on datasets including DL3DV and ScanNet++, which cover both indoor and outdoor scenes. Please refer Appendix D for the detailed training setup, and Appendix C.2 for model acceleration.
Incremental Tracking with Submaps. While our model can theoretically process an arbitrarily long sequence, we observe in practice that cumulative drift increases with sequence length, L, re- sult of catastrophic forgetting recurrent models. To ensure robust tracking, we partition the input stream into shorter subsequences (submaps). For each submap, the hidden state is re-initialized; consequently, all predicted poses {T:} are expressed in the coordinate frame of that submap's first frame. A one-frame overlap between consecutive submaps allows us to compute the relative trans- formation between them, enabling to chain the local pose estimates into a continuous trajectory. This overlap also provides an explicit iuter-submap alignment constraint, which is later incorporated into the pose graph for global optimization.
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4.2 LOOP CLOSURE VIA HIDDEN STATE
Monocular SLAM systems inevitably suffer from accumulated pose and scale drift. To address this, we introduce a novel mechanism to compute a geometric constraint between the current frame and a past frame with single forward pass when the camera revisits previously mapped area (a loop closure TTintotas al., 2022)). ). This enables pose graph optimization to produce a globally consistent trajectory, mitigating the long-standing problem of drift.
Bag of Hidden States as Long-Term Memory, As our recurrent model processes frames sequen- tially, the hidden state, M, incrementally aggregates local, multi-frame geometric and visual infor- mation. The hidden state becomes a rich, contextual summary of the local scene that the system has just observed. We leverage this by caching the final hidden state Ma for each submap C, in a bag of hidden states. When the system later revisits an area, can retrieve a historical hidden state from the bag of hidden states to reload past geometric and visual context.
Loop Frame Feed-Forwart for Relocalization. The process is triggered when a loop candidate is detected between the current keyframe L, of submap C. and historical keyframe L; of submap Co using an appearance- aopearance-based method (Izquierdo & Civera, 2024). We retrieve the cached hidden state Ma associated with the historical submap C. containing keyframe I,. Intuitively, conditioning on M, encourages the model to interpret I in the coordinate system of the past submap Ca. yielding a direct cross-submap constraint. A single forward pass [(, Ma) on the current frame I conditioned on this past context yields two key outputs: (1) the relocalized pose To E SF(3), and (2) the corresponding point cloud interpretation Pj = }.
J. The relative pose is then computed as T (IŞ)- To resolve scale ambiguity, compare this historical interpretation Pj against Pj- {nl which is the point cloud generated from the standard, incremental tracking of frame I, (i.e., using the current hidden state). Crucially, since both point clouds are in the camera coordinate frame and originate from the same image Ij. they differ only by a scale factor. This allows us to robustly solve for the relative scale s" via least-squares:
argmin Σ 1 -8 -44
(12)
Finally, the estimated scale and relative pose combined into the complete Sim(3) loop closure constraint:
8*
R o"
Hy-:
(13)
where Ry-ss and li- are the rotation and translation components of T
Pose Graph Optimization. The computed Sim(3) constraint enables global optimization of the entire trajectory via a pose graph. In this graph, nodes represent the keyframe poses T E Sim(3), and edges represent three types of geometric constraints: Sequential Constraint, a factor linking consecutive frames within a submap, computed as the relative transformation (TT Tx+1); Inter- Submap Constraint, an alignment factor connecting adjacent submaps, which is derived by estimat- ing the relative scale between the two point cloud predictions of their shared frame; and our novel Loop Closure factors that connect distant, revisited parts of the trajectory. The globally optimal set of poses TW* is found by minimizing non-linear least-squares cost function over all constraints:
T TW"
arg min T
Σ |llo (H7 ((T I| (KJ)EE
(14)
where the residual error is computed in the Lie algebra sim(3) using the logarithmic map log(.). This formulation finds the trajectory that best satisfies all geometric constraints simultaneously. We solve this efficiently using GTSAM (Dellaert & Contributors (022), and the resulting corrected poses are passed to the backend to update the 2DGS map, as detailed in $4.3
4.3 2DGS MAP OPTIMIZATION
The backend runs in a separate thread and incrementally builds and optimizes a globally consistent 2DGS map. For each new keyframe, takes input the RGB image It, the globally optimized cam- pose Tj E Sim (3), and the per-pixel 2DGS map &R of Ta predicted by the frontend. The backend

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pipeline then consists of four key stages: (1) pre-preeee pre-proeessing the dense predictions via adaptive elization; (2) merging the 2DGS map of new frame into the global map: (3) applying a lightweight local refinement; and (4) executing global map conrections after a successful loop closure.
Adaptive Voxelization. We empirically found that the per-pixel 2DGS predicted by the frontend is sometimes overly dense. To reduce memory consumption, first process each incoming 2DGS map G% with adaptive voxelization filter prior merging. The map is partitioned into blocks of 2 x 2 2DGS primitives. Primitives within each block are consolidated into a single merged primitive by averaging their attributes:
1 N
O mergod
Σ On
for 0 E (,,s
(15)
>n align((n.:) rı)
T mergged
(16)
n-l align(r (ri, ri)ll
Tw,
where align(-) is the standard process to ensure consistent quaternion averaging. To preserve geo- metric details, blocks with depth variation exceeding threshold Ta are excluded from this process.
Map Fusion. With each new frame, we first maintain the existing global map by pruning erroncous Gaussians. This is done by rendering the map from the current camera pose T3 using the formula from $ and removing any primitive that contributes to pixels with high RGB or depth reconstruc- tion error. Subsequently, the incoming voxelized 2DGS primitives are fused into the global map. First, they are transformed from camera to world coordinates:
Hworld = St. Ry,Ra + tk. Fworld
qk
Team
(17)
where (sk: Rx. tx)) are components of Ty: Qs is the quaternion form of Rg. To avoid redundant den- sification, we only add these new primitives in regions that are not yet well-reconstructed, identified by rendering an accumulation map from the global map and checking against threshold Taceoo
Lightweight Map Refinement. A key advantage of our Predict-and- paradigm is the dra- matically reduced optimization workload for the backend. 'The high-quality per-frame predictions from our frontend serve as strong geometric and appearance prior. Consequently, after the fusion step, we only need to refine the local map region associated with the latest K keyframes for only 20 iterations. This stands in stark contrast to existing DDGS-SLAM methods that require hundreds to thousands of optimization iterations per frame, especially during initialization.
Loop Correction of Gaussian Map. As described in $4.2 upon receiving the set of globally optimized poses T after a loop closure, the backend initiates map correction to ensure the 2DGS map aligns with the corrected trajectory. A naive approach would be to re-run rendering- optimization using the corrected poses, but we found this to be prohibitively slow. Therefore, we adopt more efficient rigid transformation strategy. We rigidly bind the 2DGS primitives to their originating keyframe. When a keyframe's ' pose is updated from Tola to Tnew, we compute the delta transformation AT Thew T.d and apply it all associated primitives. This process efficiently warps the map to align with the corrected trajectory without costly re-rendering
5 EXPERIMENTS
5.1 EXPERIMENTAL SETUP
We evaluate our system on three challenging real-world datasets: ScanNet (Dai et al. 70) BundleFusion (Dai et al. 2017b), and KITTI (Geiger et al. 2012). ScanNet and BundleFusion consist of large-scale indoor scenes with motion blur and diverse lighting conditions. We treat ScanNet as in-domain evaluation and BundleFusion as out-of-domain evaluation. KITTI features large-scale outdoor driving scenarios with high scale variance and dynamic objects. We evaluate tracking accuracy using Absolute Trajectory Error (ATE RMSE) and rendering quality via PSNR, SSIM, and LPIPS. Since monocular SLAM has inherent scale ambiguity, we compute ATE after Sim alignment to ground truth. For ScanNet and BundleFusion. further evaluate geometric quality with scale-aligned Depth L1 error. All experiments are conducted on a single RTX 4090 GPU paired with Intel Xeon 6133 CPU (2.50GHz).
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Ground Truth
Ours
DepthGS
MonoGS
SJPO-GS
Scanad
Scannet
Bundle Tusion
aptil
DundieFusion office2
MERRO
Figure 3: Qualitative Rendering Results.
Baselines. We compare Flash-Mono with three state-of-the-art monocular GS-SLAM systems on both mapping and tracking quality: MonoGS (Matsuki et al. 2024), DepthGS (Zhao et al. 2025). and S3POGS (Cheng et al.] (025). We also compare against leading monocular SLAM systems renowned for pose accuracy, although they do not produce dense renderings, including ORB- SLAM3 (Campos al.) 2021 DROID-SLAM (Teed & Deng 2021), and MASt3R-SLAN (Murai al. 2025). On KITTI, we primarily compare against S3POGS, we encountered frequent failures while evaluating other focused GS SLAM basclines due to the large-scale and high dynamic nature of KITTI.
5.2 TRACKING PERFORMANCE
As shown in Table I Flash-Mono significantly outperformed all traditional and GS-SLAM base- line methods. On most scenes, we also surpassed HASI3R-SLAM, a recent feed-forward SLAM system. This validates the effectiveness of multi-frame context and our novel hidden-state-based relocalization mechanism.
Table 1: ATE RMSE (cm) on ScanNetV1 and BundleFusion datasets. Lower is better. We mark the first and second best results.
ScanNetV1
BundieFusion
ATE [cm]]]
0054
0059
0106
0169
0233
0465
aptO)
apt2
copyroom
officel
office2
ORB-SLAM3
243.26
90.67
178.13
60.15
25.01
181.86
87.37
265. 665.64
27.60
116.33
49.33
DROID-SLAM
161.22
69.92
89.11
28.26
74.01
117.27
89.38
148.04
19.71
31.41
73.91
MonoCS
70.19
97.24
150.89
191.98
62.45
113.19
122.59
142.54
53.41
62.67
127.02
DepthGS
192.18
93.69
140.19
205.92
81.90
121.01
67.52
119.74
14,59
40.42
16.05
S3PO-GS
69.36
16.52
26.15
87.04
27.09
96.35
92.49
97.90
21.88
64.22
69.88
MASI3R-SLAN
13.25
10.89
15.83
15.24
10.99
15.74
9.65
13.66
9.28
9.97
9.92
Ours
11.69
8,89
10.83
10.16
12.13
13.00
11.44
12.36
7.34
8.74
9.34
5.3 MAPPING PERFORMANCE
Table presents the rendering quality results. Although perform only 20 optimization iterations per keyframe (a 10x reduction compared to the 250 iterations used by MonoGS (Matsuki et al. (024) and S3PO-GS (Cheng et al. 2025)), our method achieves superior competitive rendering quality.
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Table 2: Mapping quality on ScanNetV1 and BundleFusion Higher better for SSIM/PSNR, lower is better for LPIPS. We mark the first and second best results.
ScanNetV1
BundieFusion
Method
Metric
0054
0059
0169
0233
0465
FPS 1
apto
apt2
copyroom
office0
office2
FPS t
SSIM
0.80
0.74
0.72
0.77
0.68
0,39
0.70
0.39
0.70
0.52
0.60
MonoGS
LPIPS L
0.61
0.60
0.54
0.06
0.67
0.74
0.69
0.67
0.82
0.63
0.78
0.71
1.00
PSNR
19.24
16.54
16.00
18.86
17.65
14.52
13.68
11.50
14.37
13.38
13.96
SSIM
0.31
0.32
0.34
0.42
0.36
0.26
0.38
0.41
0.58
0.56
0.58
DS
LPIPS +
0.79
0.78
0.78
0.73
0.84
0.81
1.57
0.67
0.69
0.51
0.62
0.63
1.28
PSNR
12:29
12.42
11.76
13.64
13.17
11.11
13.65
14.85
17.00
15.96
16.51
SSIM
0.80
0.71
0.75
0.78
0.73
0.61
0.74
0.64
0.47
0.63
0.64
S3PO-GS
LPIPS
0.62
0.58
0.54
0.55
0.09
0.75
0.71
0.57
0.71
0.78
0.71
0.64
0.94
PSNR
20.79
17.19
17.60
18.52
18.37
14.14
18.98
15.72
18.56
15.23
16.59
SSIM
0.79
0.66
0.72
0.73
0.69
0.66
0.66
0.60
0.72
0.69
0.64
Ours
LPIPS
0.39
0.41
0.43
0.39
0.44
0.45
12.71
0.49
0.54
0.45
0.50
0.51
11.99
PSNR
21.73
17.83
17.75
18.52
21.60
19.51
19.03
16.48
19.50
17.10
17.63
Ground Truth
Ours
DepthGS
MonoGS
S3PO-GS
BundleFusion
officed
Scannet
BundleFusion
copyroom
Figure Qualitative Analysis on Rendered Depth.
This highlights the effectiveness of our Predict-and-Refi paradigm: high-quality Gaussians pre- dicted by our foundation model reduce the need for costly backend optimization. The scale-aligned Depth L1 error is evaluated in Table [5] We achieve a lower Depth LI error, suggesting a more accurate underlying 3D scene reconstruction. Qualitative rendered RGB and depth are presented in Figure 3 and Figure
5.4 OUTDOOR EVALUATION ON KITTI
We further evaluate Flash-Mono on the KITTI benchmark to assess generalization to large-scale outdoor environments. Since MonoGS and DepthGS are designed primarily for indoor scenes, they often fail under the large scale variance and dynamics in KITTI; therefore, we mainly compare with S3PO-GS (Cheng et al. (225). which is designed for outdoor scenarios. Table 3 reports tracking accuracy and Table reports rendering quality.
Table 3: ATE RMSE (m) on KITTI Odometry. Lower is better.
ATE RMSE [m]]
00
05
06
07
08
28
Ours
12.85
16.58
9.93
12.08
45.25
16.75
S3PO-GS
32.49
34.76
16.43
fail
64.74
23.64

---

Table Rendering quality on KITTI Odometry. Higher is better for PSNR/SSIM, lower is better for LPIPS.
Method
Metric
00
05
06
07
08
28
PSNR f
16.65
15.64
13.55
fail
17.25
15.30
S3PO-GS
SSIM
0.5409
0.5320
0.4726
fail
0.5912
0.5053
IPIPS 4
0.6254
0.6352
0.7241
fail
0.4626
0.6131
PSNR
17.41
17.01
15.13
17.89
16.12
17.47
Ours
SSIM f
0.6584
0.6278
0.5922
0.6036
0.6221
0,5633
LPIPS
0.5358
0.4871
0.5333
0.4854
0.4710
0.4581
Cosseian 68
Mean Depth LI Error (m) on ScanNet and BundleFu- sion. We mark the best results.
Table
ATTE RAASE tel
0.0029
ATE
01819
3116
L1(m)
Scan.
Bundle.
25 Reline Heratons
PnP Loop Closure Methed
Clip Length (8)
PSNR (di))
(4)
MonoGS
1.19
1.20
Figure 5: Ablation studies. (a) Refine Iterations vs. PSNR. (b) Submap Length vs. ATE RMSE. (c) Loop Closure Set- tings. (d) PSNR vs. Model Size.
DepthGS
0.49
0.23
S3PO-GS
0.52
0.85
Ours
0.34
0.21
5.5 ABLATION
We conducted ablation studies to analyze the impact of key system components. The results are shown in Figure (5 First, we evaluated the effect of backend refinement iterations rendering quality (PSNR). Without refinement (0 iterations), the direct output from our seed-forward model achieves a PSNR of 20.14. Applying 10 refinement iterations increases the PSNR to 22.41, indi- cating that the model provides strong initial prediction that can be efficiently improved by a few optimization steps. Second, we examined the influence of submap clip length on tracking aceu- racy (ATE RMSE). The lowest error of 0.106 was observed with clip length of 8 frames. Shorter lengths resulted in higher error, suggesting insufficient temporal context, while lengths greater than 16 frames also increased the error, which points to the accumulation of intra-submap drift caused by the forgetting characteristic of RNN models. This supports the strategy of partitioning the input stream. Third, we compared our hidden state- based loop closure against traditional PnP+RANSAC baseline and configuration with no loop closure. Our original system beats the other two settings on tracking performance by large margin, suggesting constraints. Finally, our adaptive voxelization module reduced the total number of Gaussian prim- itives by over 58% (from 1.35M 0.56M), which corresponded 19.70 to 19.44. This demonstrates the module's role in creating a more compact map representation small cost to rendering fidelity.
approach generates
accurate Sim(3)
minor PSNR decrease from
CONCLUSION
We presented Flash-Mono, real-time monocular Gaussian Splatting SLAM system that fundamen- tally shifts from the tume-conumiigg Train-from-Scratee paradigm to an efficient Predict-ande Predict-and-Refine Refine approach. As our experiments show, Flash-Mono achieves state-of-the-ar rendering quality with a 10x reduction in computation time. Furthermore, we introduced novel loop closure mechanism that enables robust Sim(3) optimization to correct scale and pose drift inherent in monocular sys- tems, leading to superior tracking accuracy on complex indoor scenes.
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ACKNOWLEDGMENTS
This work was supported in part by the National Natural Science Foundation of China (NSFC) under Grant 62403142, and in part by the Science and Technology Commission of Shanghai Municipality under Grant 2451 I 103100.
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A LLM USAGE STATEMENT
In the preparation of this manuscript, we utilized Large Language Models (LLMs) as assistive tools, in accordance with the ICLR policy. The specific roles of these models detailed below.
We employed GPT-5 primarily for language polishing and refinement. After drafting the paper, used the model to improve grammar, clarity, and overall readability. The core ideas, experimental setup, results, and conclusions were conceived and articulated entirely by the human authors. We reviewed and edited all model-generated suggestions to ensure the final text accurately reflects our original research and contributions.
We also used Gemini 2.5 Pro with its deep research capabilities to assist in the literature review process. This tool helped in identifying relevant prior work, summarizing existing literature, and locating publicly available publications. All cited works were subsequently read, analyzed, and contextualized by the authors to build the foundation for our research.
The authors take full responsibility for all content presented in this paper, including its scientific va- lidity, accuracy, and originality. LLMs were used strictly productivity tools and are not considered authors of this work.
B MORE EXPERIMENTAL SETUP AND RESULTS
B. BASELINE
For MASt3--SLAM (Murai al. 2025), we adopted the official experimental configuration with w. = 0.333, 0.1, w. = 0.005, and maximum of 10 matching iterations. For MonoGS (Mat- suki et al. 2024), we followed their TUM settings, as TUM shares the closest characteristics with the BundleFusion (Dai et al.) (017b) and ScanNerV1 (Dai et al.) (0177) datasets. During evalua- tion, MonoGS encountered out-of-memory (OOM) failures on three sequences: ScanNet 0054 and 0465, and the aptO sequence in BundleFusion. For these cases, the reported metrics are computed only on the subsequences successfully reconstructed before the crash. This truncation may lead to optimistic bias, as drift accumulation over the full sequence would likely degrade reconstruction and rendering quality further. For S3PO-GS (Cheng et al., 2025), we used their official base config- uration while loading the ground-truth intrinsics for the test datasets. For the ScanNetVI sequence 0465, accumulated pose drift caused Pn. failure, and results are reported only on the valid subse- quence. For DepthGS (Zhao et al.) 2025), we followed the official repository guidelines, generating monocular depth maps for each sequence using the UniDepthV2-large checkpoint and benchmark- ing under the provided experimental settings. Importantly, the reported FPS includes the runtime required for UniDepth (Piccinelli et al. 2025), ensuring fair comparison across methods.
B.2 COMPARISON DETAILS ON DEPTH RENDERING QUALITY
As shown in Table 6 we record the depth rendering results in detail. To avoid bias caused by large emoos in failure scenarios, we report the main text the mean values excluding the maximum and minimum.
Table 6: Depth LI Error (m) on ScanNetV1 and FundleFusion datasets. Lower is better. We mark the best results.
ScanNetV1
BundleFusion
Depth L1 [m] L
0054
0059
0106
0169
0233
(465
apt0
apt2
copyroom
office0
office2
MonoGS
1.06
1.27
1.41
1.56
0.89
0.82
0.96
1.28
1.18
1.15
1.26
DepthGS
0.45
0.66
0.52
0.48
0.41
0.47
0.37
0.29
0.13
0.18
0.21
S3PO-GS
0.58
0.35
0.55
0.66
0.28
0.89
0.72
0.99
0.41
1.01
0.85
Ours
0.16
0.23
0.51
0.17
0.35
0.44
0.33
0.35
0.11
0.11
0.18
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B.3 MORE QUALITATIVE RESULTS
Figure 6 provides a qualitative comparison of camera trajectories from different methods. We plot the estimated trajectory (colored line) against the ground truth (dashed gray line), projected onto the XY plane. The color of the path indicates the magnitude of the error, following a gradient from blue (low error) red (high error).
Figure 7 provides qualitative comparison of the reconstructed map on ScanNet scene 0054. Scene 0054 multi-room apartment with varying lighting conditions. All baselines failed to reconstruct the scene.
MasBB-SLAM
S3PO-GS
DepthCS
MenoCiS
Scene 0053
Sccue 0209
Sccne
Scene 0108
Sccve 0233
Scone 0445
Figure 6: Qualitative Analysis on Estimated Trajectory
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MonoCS
DepthGS
Ours
Figure 7: Qualitative Analysis on reconstructed ScanNet scene 0054. All baselines failed to reconstruct the scene.
C MODEL SIZE AND ACCELERATION
C.I MODEL SIZE
To address concerns regarding the feasibility of deployment on resource- consrrce-onstriieee devices (e.g., edge devices or laptops), we provide a detailed breakdown of our model size and performance analysis on lower-end hardware.
Model Size and Memory Usage. The detailed parameter breakdown of our architecture is pre- sented in Table 7 The complete model consists of 795.7 million parameters. terms memory consumption, the system requires approximately 3GB of VRAM during inference.
Table 7: Detailed breakdown of Flash-Mono model parameters.
Component
Total Parameters
Encoder
303.1 1 M
Decoder
380.8 M
Heads & Tokens
111.8 M
Total
795.7 M
C.2 MODEL ACCELERATION
The fced-forward frontend is the primary computational bottleneck in the Flash-Mono system. To enhance its practicality for SLAM applications more accessible, resource-conssrained hardware, we tested the effectiveness of several acceleration methods.
First, converted the attention module parameters from float32 to float16 precision. This strategy compresses the model size and accelerates inference without degrading downstream task accuracy. Second, addressed inefficiency in the single-image inference pipeline. With batch size of 1,
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frequent CPU-side operator launches create bottleneck that underutilizes the GPU. By employing CUDA Graphs, we merged multiple operator calls into single, efficient launch. See Figure g
To validate these improvements under resource- esouuce-constrraine SLAM setting, benchmarked the system on a laptop version NVIDIA RTX 4060 GPU (8GB). These optimizations reduced frontend inference latency from 283 ms to just 85 ms, 3.33x speedup. Notably, the inference time the laptop RTX 4060 after acceleration (83 ms) is comparable to the inference time on the high-end RTX 4090 (24GB) used our main experimental setup (62 ms). In addition, our model is based on the transformer architecture, further optimizations, such as quantization and efficient attention mechanisms, remain promising directions for future inference acceleration.
Frontend inference w/o CUDA Graph
L tie Uhe
GPU Pipeline:
opeeraorr launches become the bottleneck
CPU Pipeline:
CUDA Graph
Launch A
Launch
Launch
Launch
One Graph Launch
Frontend inference w/ CUDA Graph
Pipeline with Speed-up:
Goph (GraphExec 2)
Groph (Gophiiee
Graph 10
Figure 8: CUDA Graph optimization
D TRAINING SETUP
D.1 DATASETS
We train our model on combination of indoor and outdoor datasets, including ScanNet++, DL3DV, and Replica. For each training sequence, we utilize the provided RGB video stream, ground truth camera poses, and depth maps. The ground truth point cloud(μ, required for supervising the ge- ometry loss is generated by unprojecting the ground truth depth map D, using the corresponding camera intrinsics K.
D.2 EXTRA RENDERING LOSS
While the pose and geometry loss terms adhere to the standard formulations outlined in the main paper. the rendering loss incorporates a more sophisticated strategy. Our empirical investigation revealed that naive rendering loss, computed solely on the merged Gaussian point cloud from an entire sequence, encourages the model to excessively shrink the scale of individual Gaussians to prevent inter-frame conflicts. In our incremental SLAM setting, this will lead to bad rendering result on the first few frames each submap. Thus, our rendering loss consists two parts with the same weights. The first is per-frame rendering loss, where the predicted 2DGS map for each frame, Gt, is rendered independently and compared against its corresponding ground truth image and depth. For the second rendering loss, the 2DGS predictions from the entire input sequence, {G, 1 are merged into global 2DGS map and then rendered and supervised against the ground truth RGB and depth.
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D.3 TRAINING CURRICULUM
We designed a three-stage training curriculum. We first warm up the GS head for 5,000 steps. In this initial phase, we initialize our model parameters from CUT3R (Wang et al. 2025b) and freeze all network parameters except for the final 2DGS attribute prediction head and employ relatively high learning rate of 2 10-4 on short imput sequences of 1-4 frames. The freezing prevents the gradients from excessive rendering loss from backpropagating into the well-trained model backbone. Following this, we unfreeze the parameters of the decoder, the pose head, and the point-mean prediction head, while significantly reducing the learning rate to X 10 and fixing the sequence length at frames. This intermediate stage is intended to allow the model to adapt to the specific data distribution of the task and mitigate the risk of gradient explosion common in recurrent architectures. Finally, we adapt the model to longer sequence. The learning rate is further decreased to 5 x 10 and the maximum sequence length is extended to 32 framess
E DETAILED RUNTIME BREAKDOWN
To substantiate the claimed efficiency and clarify the FPS calculation protocol mentioned in the main paper, we provide comprehensive runtime breakdown of Flash-Mono.
Total Frames The reported FPS is end-to-end metric, calculated Total Runtime This metric explicitly accounts for all system components, including frontend inference, backend map refinement, loop closure detection, pose graph optimization (PGO), and other system overheads.
FPS Calculation Protocol.
Runtime Analysis. Flash-Mono operates using parallel threads: Frontend thread responsible for tracking and loop closure, and a Backend thread handling mapping and refinement. The detailed time consumption for each module is presented in Table 8
As shown in the table, the Backend thread (77.5 ms per frame) is slightly slower than the Fron- tend thread (65 ms per frame), making it the bottleneck of the system. It is important note that computationally intensive loop closure operations (such as Loop Frame Feedforward and PGO) are sparse events. Consequently, their amortized cost is minimal, allowing the system maintain high real-time performance.
Table 8: Runtime breakdown of Flash-Mono. The system runs in parallel threads, with the Backend being the primary bottleneck. Loop closure operations are sparse events.
Thread
Module
Time (ms)
Note
Feedforward Inference
62
Per frame
Loop Closure Detection
3
Per frame
Frontend
Total (Per Frame)
65
Loop Frame Feedforward
62
Per loop closure Per loop closure
PGO (Sim3 Optimization)
32
Merge & Voxelization Refine
0.5
Per frame
77
Per frame
Backend
Total (Per Frame)
77.5
GS Correction
2
Per loop closure
F ANALYSIS OF GAUSSIAN MAP COMPACTNESS
To evaluate the spatial efficiency of our map representation, conducted quantitative analysis of the total number of Gaussian primitives required to represent a complete scene. This metric provides insight into the trade-off between reconstruction quality and memory usage.
Table presents a comparison of the total Gaussian count against several state-of-the-art dense SLAM and Gaussian Splatting methods three sequences from the TUM RGB-D dataset. As
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illustrated in Table 9 Flash-Mono maintains moderate level of map compactness. The baseline statistics are sourced from CaRtGS (Feng et al. (022).
Table 9: Quantitative comparison of the total Gaussian count on the TUM dataset. Our method maintains balance between map density and compactness.
Method
fr1/desk
fr2/xyz
fr3/office
MonoGS
26.64k
43.59k
35.24k
Photo-SLAM
40.00k
0.10m
81.16k
SplaTAM
0.96m
6.36m
0.79m
Gaussian -MLA
0.76m
0.69m
1.47m
GS-ICP-SLAM
0.53m
1.91m
2.09m
Ours
0.63m
0.98m
0.61m
G ANALYSIS OF HIDDEN STATE FOR LIFE-LONG MAPPING
While the hidden state mechanism has been demonstrated to be highly effective for loop closure relocalization and in-session long-term consistency (as shown in our ablation study in the main paper), we believe this mechanism can be naturally extended to address the challenges of life-long mapping- the ability to construct and maintain an up-to-date map as the environment changes over time (e.g., furniture rearrangement, lighting variations, seasonal changes).
Core Challenges. Life-long mapping presents two fundamental challenges that must be addressed to maintain consistent and accurate representation of a dynamic environment:
Challenge 1: Relocalizing Against an Outdated Map. The system must capable of relocalizing a new observation of the changed environment against an old, potentially outdated map. Our hidden state mechanism can address this challenge using the same feed-forward relocalization approach described in Section 4.3 of the main paper.
To demonstrate this capability, we conducted a case study on scene that underwent significant en- vironmental changes. We first input 8 frames captured at night (with curtains closed and a seat back in place) to generate a hidden state Mmight representing the historical environment. Subsequently, we fed the model with new observation of the same scene captured during daytime, where the curtains were open and a person was sitting in the chair. As illustrated in Figure 9 our feed-forward model successfully relocalized the new frame against the outdated hidden state and predicted geometrically consistent results, despite the substantial appearance and content changes.
This result suggests that our current architecture already possesses inherent robustness environ- mental variations. We anticipate that training specifically on datasets featuring temporal changes (e.g.. time-of-day variations, seasonal shifts) would further enhance this capability.
Challenge 2: Updating the Map Representation. Beyond relocalization. the system must update its map representation with new observations to remain synchronized with the current environment state. Our system maintains three core representations: the Gaussian Map, the Pose Graph, and the Hidden State (which serves as compact submap descriptor). While strategies for updating Gaussian primitives (Fu et al. 2025) and pose graphs (Zhao et al. (2021) to changing environment are well-established in the SLAM literature, updating the hidden state descriptor in a life-long setting presents unique challenges.
naive approach of continuously aggregating new observations into a fixed-capacity hidden state vector inevitably leads to saturation and catastrophic forgetting of historical information. To address this challenge for life-long mapping, we propose two potential strategies:
1. Discrete State Replacement. A straightforward yet effective strategy is to detect significant environmental changes during relocalization (e.g.. via monitoring the photometric residual or geo- metric consistency). When substantial changes are identified, rather than attempting to update the obsolete hidden state Mala- we generate a fresh hidden state Mfew from the current observations. This new state either replace the outdated state in the Bag of Hidden States or be appended as
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alternative descriptor for the same physical location, effectively maintaining multiple hypotheses (e.g., "daytime" vs. "nighttime" appearance).
2. Model Adaptation Instead of discrete replacement, can implement continuous update strat- egy inspired by TIT3R (Chen et al. 2025). This approach reframes the hidden state update as an online learning problem, treating the state "fast weights" optimized via gradient descent during inference. Crucially, TTT3R introduces a confidence aafidenne-guided guided update rule, where the learning rate dynamically derived from the alignment between the current memory state and the incoming ob- servation. This acts as a self-supervised gating mechanism: it allows the hidden state to selectively integrate persistent environmental changes (where alignment confidence high) while suppressing transient noise or inconsistent updates (where confidence is low). By adopting this formulation, our system can evolve to capture gradual domain shifts -uchh as seasonal changes or furniture rear- rangement-while mitigating the catastrophic forgetting typically associated with recurrent updates, ensuring the map remains both up-to-date and geometrically consistent over the long term.
Future Directions. While our current system demonstrates promising initial capabilities for han- dling environmental changes, comprehensive life-long mapping system would require dedicated training on lemporally-varying datasets and careful engineering of the state update mechanisms. We believe this represents an exciting direction for future work, building upon the flexible hidden state architecture introduced in this paper.
Input Data:
Reconstructed Scene
Context Views (8 frame)
Relor.
New observation (1 frame)
Top View
Front View
Figure 9: Case Study: Robust Relocalization Under Environmental Changes. The model gen- erates a hidden state from 8 context views captured at night (curtains closed, empty chair). When presented with new observation from the same location but under drastically different conditions (daytime, curtains open, person sitting), the feed-forward model successfully relocalizes and re- constructs accurate geometry. This demonstrates the hidden state's potential for life-long mapping scenarios where environments undergo temporal changes.
20


Reviews:
[{"review_id": "FDYR782Q0Q", "reviewer": "Reviewer_S5jM", "summary": "The paper introduces Flash-Mono, an monocular 3D Gaussion Splatting SLAM that employs the feed-forward paradigm to improve the accuracy and speed of GS-SLAM methods. The propose method trains a recurrent feed-forward fronted to predicts local camera posed and per-pixel 2D Gaussions and cobmines it with backend global mapping and loop closure to achieve final tracking and mapping results. The experimental results demonstrate the superiority of the proposed Flash-Mono.", "questions": "1.When the backend mapping fusion and refinement correction are performing, what frequency does the front-end and backend interacts and how it influence the final results.\n\n2.How the historical interpretation P_a^j is calculated, project point cloud from current frame I_j to historical submap C_a?\n\n3.Does the pose relocalization process perform after the local map refinement or before, what is the detailed pipeline of the whole system?\n\n4.In Loop Correction of Gaussian Map, does this simple transformation of 2DGS primitives generate bad overlap between 2DGS, resulting in bad rendering and mapping.", "limitations": "", "rating": 2, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "The authors introduce a dedicated feed-forward Gaussion prediction based SLAM framework with sophisticated frontend and backend component designs to improve tracking and mapping performance in various scenes. The designs are reasonable and well-supported by and experimental results.", "weaknesses": "1.The feed-forward reconstruction-based SLAM is new direction for SLAM community, the authors should conduct extensive experiments to show its superiority and weakness on various SLAM scenes. That is, the authors should provide more results on indoor and outdoor benchmarks and mapping reconstruction metric like Completion and Chamfer.\n\n2.The authors do not provide a sufficient comparison with other feed-forward SLAM method, e.g., VGGT-SLAM. Additionally, the ablation results are too simple and no failure analysis about the limitations of the proposed method.\n\n3.The explanation of some technical details is unclear. What data is used to train the frontend network, how the training data influence the final SLAM results?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "yXwGfueoQg", "reviewer": "Reviewer_eTqG", "summary": "This paper presents Flash-Mono, a monocular SLAM system that integrates a feed-forward model with 2D Gaussian splatting (2DGS) for real-time scene reconstruction and camera tracking. The key idea is to replace the traditional per-frame optimization of Gaussian attributes with a recurrent network that directly predicts poses and Gaussians from sequential inputs. The proposed system consists of three modules: a feed-forward frontend, a 2DGS-based mapping backend, and a loop closure mechanism based on hidden states. The authors claim a 10× speedup over existing GS-SLAM methods while achieving state-of-the-art performance in tracking and rendering quality on ScanNet and BundleFusion datasets.", "questions": "1. How does the method perform in outdoor or large-scale scenes where scale variation and dynamics are more challenging?\n﻿\n2. Could the hidden state be further exploited for lifelong mapping or incremental learning beyond submap-based reset?\n﻿\n3. Have you considered comparing with non-GS SLAM systems in terms of robustness under motion blur or low-texture scenes?\n﻿\n4. What is the memory footprint of the model, and is it feasible for mobile or embedded platforms?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The combination of a recurrent feed-forward model with 2DGS for monocular SLAM is novel. The use of hidden states as submap descriptors for loop closure is creative.  \n\nThe method achieves strong results in both tracking (ATE) and rendering (PSNR, SSIM, LPIPS), outperforming recent GS-SLAM systems.  \n\nThe paper is well-organized and easy to follow.", "weaknesses": "The evaluation is limited to indoor datasets (ScanNet, BundleFusion). It is unclear how the method generalizes to outdoor or large-scale environments.  \n\nWhile the hidden state mechanism is innovative, its capacity for long-term consistency is not deeply analyzed.  \n\nThe paper does not discuss model size or memory usage, which are important for deployment on resource-constrained devices.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "7jYWpIlndw", "reviewer": "Reviewer_5jYg", "summary": "This paper presents a Gaussian-based SLAM system that adopts a feed-forward paradigm to predict the attributes of 2D Gaussians and relative poses by leveraging multi-frame contextual information. The core contribution lies in integrating a feed-forward module into the GS-based SLAM framework, effectively replacing the time-consuming optimization process used in most recent GS-based SLAM systems. In addition, a loop closure module based on the hidden states of keyframes is introduced to enhance tracking accuracy and mitigate error accumulation. Experimental results demonstrate both the effectiveness and efficiency of the proposed SLAM system.", "questions": "Please refer to the weakness part.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "1. The paper is well-written and easy to follow, and its motivation is clear and well-founded.\n2. It introduces a novel feed-forward paradigm for Gaussian-based SLAM, replacing traditional optimization-based processes and significantly improving efficiency. By eliminating costly optimization steps, the proposed method contributes to the development of real-time and lightweight Gaussian-based SLAM frameworks.\n3. The paper trains a recurrent feed-forward frontend model that aggregates multi-frame visual features into a hidden state via cross-attention and jointly predicts camera poses and per-pixel Gaussian properties. The hidden state is further utilized in the loop closure module to reduce cumulative drift and enhance tracking robustness.", "weaknesses": "1. Some state-of-the-art RGB-based SLAM methods, such as Photo-SLAM and DROID-Splat, are not included in the comparison. In addition, since S3PO-GS primarily focuses on outdoor scenes, it would be beneficial to include evaluations on outdoor datasets to better demonstrate the proposed SLAM system’s capability in handling long and complex outdoor sequences.\n2. It would be beneficial to include a quantitative analysis of the total number of Gaussians required to represent the entire scene. This statistic could provide valuable insight into the efficiency of the proposed feed-forward prediction and map fusion modules, and demonstrate how effectively the method balances compactness with reconstruction quality.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "W3Lqu2j7aJ", "reviewer": "Reviewer_91zN", "summary": "The paper presents Flash-Mono, a feed-forward monocular SLAM system that integrates a Transformer-based recurrent frontend, 2D Gaussian Splatting for mapping, and a hidden-state-driven Sim(3) loop closure. It achieves high-quality reconstructions and real-time performance, claiming up to a 10× speedup over optimization-based GS-SLAM methods. Overall, the paper demonstrates strong engineering execution but requires clearer validation to fully substantiate its claims of novelty and robustness.", "questions": "Please refer to the weaknesses.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "- The paper is generally well written, logically structured, and easy to follow. Figures, tables, and ablations are clearly labeled and help readers understand both the system design and experimental results.\n\n- The paper effectively combines a recurrent feed-forward frontend with a lightweight refinement backend, offering a plausible path toward faster monocular Gaussian Splatting without fully sacrificing reconstruction quality.\n\n- The authors compare against multiple strong baselines (MonoGS, DepthGS, MASt3R-SLAM) using consistent metrics across ScanNet and BundleFusion\n\n- The work reports concrete latency optimizations such as mixed-precision and CUDA Graph execution, showing practical awareness of real-time deployment concerns.", "weaknesses": "- Although the paper emphasizes real-time performance and a 10× speedup, the reported FPS is not clearly defined. While Appendix B.1 states that DepthGS includes the UniDepthV2 inference time, it remains unclear whether Flash-Mono’s FPS also accounts for the 20-iteration refinement stage, Sim(3) optimization, loop closure, and rendering overheads. Without these components, the comparison may not reflect true end-to-end latency. A complete runtime breakdown (frontend, refinement, loop closure, rendering) and a unified timing protocol across baselines are necessary to substantiate the claimed efficiency.\n\n- The proposed system integrates several elements already established in previous works: 2DGS, Predict-and-Refine optimization (from existing feed-forward mapping schemes), and Transformer-based hidden state modeling (as seen in MASt3R-SLAM and CUT3R). However, the paper does not sufficiently disentangle which components are novel and which are adapted. The claimed contributions, feed-forward monocular reconstruction and hidden-state-based Sim(3) loop closure, lack rigorous ablation or replacement studies. For instance, the paper does not test substituting 2DGS with 3DGS, removing the hidden state, or comparing Predict-and-Refine with standard local BA. Consequently, the boundary of novelty remains unclear and the contribution feels primarily engineering efforts rather than new approaches.\n\n- The evaluation focuses solely on indoor datasets, i.e. ScanNet V1 and BundleFusion, which substantially overlap with the training domains (Replica, ScanNet++, DL3DV). It is recognized that generalization to outdoor or large-scale environments may be limited due to mismatches in scene/depth scales. As a practical compromise, it is recommended to include additional experiments on unseen indoor/hybrid benchmarks such as TUM RGB-D, 7-Scenes, and ETH3D to more comprehensively assess the model’s robustness and effectiveness.\n\n- The method relies on a CUT3R backbone pretrained on ScanNet v2. V2 mainly differs from V1 in labeling quality, where scenes between CUT3R’s training data and the evaluation sets used in this paper are still the same. Please justify this overlap/leakage.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)