Current Vision-based SLAM systems fail catastrophically when motion blur corrupts the visual input, as they attempt the ill-posed inverse problem of recovering sharp content from degraded observations. We present MotionGS-SLAM, which fundamentally reimagines motion blur handling through a paradigm shift: rather than removing blur artifacts, we reformulate the challenge as a well-constrained forward problem that generatively models blur formation within the rendering pipeline. By leveraging event cameras' microsecond temporal resolution and immunity to motion blur, we introduce a novel event-modulated Gaussian kernel that dynamically adapts each Gaussian's rasterization based on precise motion cues. Our dual-modulation mechanism transforms 2D Gaussian projections from isotropic dots into anisotropic, motion-aligned elliptical brush strokes (spatial modulation) while adaptively varying exposure integral sampling density based on local velocity (temporal modulation). This physics-based approach enables joint optimization of intra-exposure camera trajectories and 3D scene geometry through blur-aware photometric and event-based constraints. Extensive experiments demonstrate significant improvements over state-of-the-art methods in trajectory accuracy and map quality under severe high-motion conditions.
Figures & tables
Fig. 1: MotionGS-SLAM in action. Existing Vision-based SLAM systems fail in challenging high-motion scenarios due to severe motion blur in the input frames (top row). Our method leverages the co-located event stream to explicitly model the physical process of blur formation. This allows MotionGS-SLAM to robustly track the camera and reconstruct sharp, high-fidelity scenes (bottom row) directly from the degraded imagery, where other methods would fail.
Fig. 2: Overview of the MotionGS-SLAM pipeline. Our system takes a blurry RGB image and a stream of events as input. The core of our method is the Event-Modulated Gaussian Kernel , which leverages events to physically model motion blur. (1) Event-Gaussian Association: For each projected Gaussian, we use a 4D spatio-temporal hash grid to efficiently query associated events. From these, we compute local motion statistics (velocity νj , direction ϕj ). (2) Kernel Modulation: These statistics drive our novel dual-modulation mechanism, anisotropically scaling the projected 2D Gaussian’s shape (spatial modulation) to replicate the motion streak. (3) Physics-Based Rendering: Our renderer uses the modulated kernels and an interpolated camera trajectory ( Tst , Ted ) to synthesize a blurred image that matches the real observation. This entire differentiable process is used in two threads: a real-time Tracking front-end that optimizes only the camera trajectory, and a Mapping back-end that performs joint optimization over a keyframe window to refine both the 3D Gaussian map and poses.
Fig. 3: Qualitative comparison of reconstructed scenes under severe motion blur conditions. The top three rows show results from state-of-the-art baselines: Photo-SLAM [ 8 ] , Mono-GS [ 13 ] , and EGS-SLAM [ 4 ] . Photo-SLAM and Mono-GS struggle significantly, producing highly blurry and distorted reconstructions with numerous artifacts and ”ghosting” effects. EGS-SLAM, while benefiting from event data, still exhibits noticeable blur and less accurate details, particularly in fine textures (e.g., the television screen, object edges). In stark contrast, the bottom row showcases results from MotionGS-SLAM (Ours) . Our method consistently reconstructs sharp, high-fidelity scenes with clear details and accurate geometry.
Method
Metric
room0
room1
room2
office0
office1
office3
office4
Avg.
Rendering FPS
PhotoSLAM [ 8 ]
PSNR[dB] ↑
17.91
18.96
–
23.15
–
17.04
14.28
–
1058.6
SSIM ↑
0.499
0.588
–
0.640
–
0.596
0.546
–
LPIPS ↓
0.460
0.455
–
0.417
–
0.391
0.526
–
MonoGS [ 13 ]
PSNR[dB] ↑
20.27
21.77
23.85
26.55
26.56
21.21
21.59
23.12
1102.1
SSIM ↑
0.612
0.677
0.748
0.769
0.810
0.729
0.749
0.728
LPIPS ↓
0.474
0.471
0.355
0.385
0.355
0.287
0.411
0.391
TABLE I: Rendering performance comparison on EventReplica . “–” indicates sequences where reinitialization did not succeed. Boldface marks the best result per column.
Fig. 4: Qualitative comparison on our real-world dataset. We demonstrate our method’s performance on challenging, low-light handheld sequences. As shown in the corresponding figure, image-only baselines like Photo-SLAM [ 8 ] and Mono-GS [ 13 ] produce blurry reconstructions with severe ”ghosting” artifacts. While the event-based EGS-SLAM [ 4 ] improves upon them, it still suffers from soft details. In contrast, our method is the only one that successfully reconstructs a sharp, geometrically consistent scene, recovering fine details.
Method
Scene 1
Scene 2
Scene 3
Avg.
Comparison with State-of-the-Art
ORB-SLAM3 [ 2 ]
8.91
35.42
41.05
28.46
PhotoSLAM [ 8 ]
11.24
9.85
28.66
16.58
EGS-SLAM [ 4 ]
4.52
5.15
4.81
4.83
Ablation Study
Baseline (MonoGS) [ 13 ]
6.88
7.12
5.95
6.65
TABLE II: Tracking Accuracy (ATE in cm) and Ablation Study on our Real-World Dataset .
Scenes
ORB-SLAM2 [ 14 ]
PhotoSLAM [ 8 ]
MonoGS [ 13 ]
EGS-SLAM [ 4 ]
MotionGS-SLAM
room0
5.57
6.64
12.76
4.85
3.98
room1
L
L
8.45
3.55
2.88
room2
L
L
3.64
3.25
2.84
office0
L
L
7.44
3.75
3.01
office1
L
L
7.78
3.66
3.06
office3
6.48
6.78
7.91
4.55
3.92
TABLE III: Tracking results ATE (cm) on EventReplica . L denotes tracking failure. Boldface marks the best result per row/average.
Variant
room0
office0
ATE ↓
PSNR ↑
SSIM ↑
LPIPS ↓
ATE ↓
PSNR ↑
SSIM ↑
LPIPS ↓
A0_Baseline
12.76
20.27
0.612
0.474
7.44
26.55
0.769
0.385
A1_Blur_Only
11.80
21.10
0.630
0.455
6.90
27.30
0.780
0.370
A2_Event_Assoc
6.10
22.20
0.700
0.340
4.10
28.50
0.830
0.240
A3_Spatial_Only
5.40
23.60
0.735
0.250
3.50
30.70
0.880
0.160
A4_Temporal_Only
5.10
23.10
0.725
0.265
3.40
30.20
0.870
0.175
TABLE IV: Ablation on EventReplica for two scenes. Best results in each column are in bold .
Fig. 5: Qualitative Ablation Study on EventReplica Dataset. Top row shows severely blurred input. Red and orange boxes highlight reconstructed details for: A2: Event data with standard losses. A3: A2 + spatial kernel modulation. A5 (Ours): Complete MotionGS-SLAM. Clear progression from blurry A2 to sharp A3, culminating in high-fidelity reconstruction with our full model.