ReconDrive: Fast Feed-Forward 4D Gaussian Splatting for Autonomous Driving Scene Reconstruction

Haibao Yu; Kuntao Xiao; Jiahang Wang; Ruiyang Hao; Yuxin Huang; Guoran Hu; Haifang Qin; Bowen Jing

cs.CVMar 8, 2026

ReconDrive: Fast Feed-Forward 4D Gaussian Splatting for Autonomous Driving Scene Reconstruction

Authors: Haibao Yu, Kuntao Xiao, Jiahang Wang, Ruiyang Hao, Yuxin Huang, Guoran Hu, Haifang Qin, Bowen Jing, +2 more

Organizations: The University of Hong Kong · Tuojing Intelligence · King’s College London · The University of Sydney · Mohamed bin Zayed University of Artificial Intelligence

Abstract

High-fidelity visual reconstruction and novel-view synthesis are essential for realistic closed-loop evaluation in autonomous driving. While 4D Gaussian Splatting (4DGS) offers a promising balance of accuracy and efficiency, existing per-scene optimization methods require costly iterative refinement, rendering them unscalable for extensive urban environments. Conversely, current feed-forward approaches often suffer from degraded photometric quality. To address these limitations, we propose ReconDrive, a feed-forward framework that leverages and extends the 3D foundation model VGGT for rapid, high-fidelity 4DGS generation. Our architecture introduces two core adaptations to tailor the foundation model to dynamic driving scenes: (1) Hybrid Gaussian Prediction Heads, which decouple the regression of spatial coordinates and appearance attributes to overcome the photometric deficiencies inherent in generalized foundation features; and (2) a Static-Dynamic 4D Composition strategy that explicitly captures temporal motion via velocity modeling to represent complex dynamic environments. Benchmarked on nuScenes, ReconDrive significantly outperforms existing feed-forward baselines in reconstruction, novel-view synthesis, and 3D perception. It achieves performance competitive with per-scene optimization while being orders of magnitude faster, providing a scalable and practical solution for realistic driving simulation.

Figures & tables

Figure 1: ReconDrive Inference Framework. ReconDrive is a powerful feed-forward 4D Gaussian splatting generation framework tailored for urban scene reconstruction and novel-view synthesis. In this framework, we select two context frames from each segment of an urban scene as input, and adopt a static-dynamic composition strategy to represent 4D Gaussians. To further enhance geometric precision, we design a dedicated Gaussian prediction head, consisting of a Gaussian Parameter Prediction Head (GPPH) and a Gaussian Center Prediction Head (GCPH), which enables the generation of Gaussians with more accurate geometric attributes.

Figure 2: Mapping Among Images, Masks, Dense Features, and Gaussian Indices. (a) Input image. (b) Dynamic object mask. (c) Dense features. (d) Gaussian kernels. Each pixel in the dense feature map generates a corresponding Gaussian kernel, enabling consistent pixel-wise mapping across all these components.

Figure 3: Visual Comparisons of Scene Reconstruction and Novel-View Synthesis. Compared with per-scene optimization methods (Street Gaussians, PVG, DeformableGS, OminiRe), our ReconDrive maintains high-quality visual rendering in both scene reconstruction (Original View) and novel-view synthesis (1–3 meters of lateral movement leftward and rightward). Compared with the feed-forward method DrivingForward, ReconDrive preserves accurate geometric consistency—evident in the tree and the surroundings—and the performance improvement becomes more pronounced as the moving distance increases. Additionally, it exhibits less image distortion and blurriness, particularly in image boundary regions. Extended visual comparisons are provided in the Appendix.

Method	Visual Scene Reconstruction			Novel-View Synthesis			Inference Speed $\downarrow$
Method	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	(Per Scene)
Per-Scene Optimization Methods
Street Gaussians Yan et al. (2024)	29.18	0.8824	0.1658	22.98	0.6959	0.2948	31min
PVG Chen et al. (2023)	29.58	0.8839	0.2200	23.48	0.6919	0.2897	23min
DeformableGS Yang et al. (2024)	28.93	0.8832	0.1610	23.73	0.6919	0.2342	46min
OminiRe Chen et al. (2025c)	29.42	0.8853	0.1577	23.01	0.6885	0.2762	35min

Table 1: Scene Reconstruction and Novel-View Synthesis Evaluation Results on the nuScenes Dataset.

Method	Detection	Tracking
Method	mAP (%) $\uparrow$	AMOTA (%) $\uparrow$
Per-Scene Optimization Methods
Street Gaussians Yan et al. (2024)	14.6	11.9
PVG Chen et al. (2023)	18.5	14.4
DeformableGS Yang et al. (2024)	16.4	13.4
OminiRe Chen et al. (2025c)	16.1	12.9

Table 2: 3D Perception Results on Novel-View Rendered Scenes with 0 m, ±1 m, ±2 m, and ±3 m View Offsets.

Appendix figures & tables8 assets

Supplementary material from the paper’s appendix.

Appendix

Figure 4: Project Loss Computing Pipeline. Note that $K_{t}$ and $K_{s}$ are identical since the camera is fixed.

Figure 5: Distribution of Spatial Distances between the Point Maps Generated by the Original VGGT and Ground Truth LiDAR Point Clouds , highlighting significant metric misalignment.

Figure 6: Qualitative Comparison of VGGT-Generated Point Maps Across Various Scale Factors , demonstrating the structural consistency despite metric scale ambiguity.

Method	Novel-View Synthesis
Method	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$
ReconDrive-S	23.54	0.6940	0.3177
ReconDrive	23.99 ( +0.45 )	0.7234 ( +0.0294 )	0.2591 ( -0.058 )

Appendix

Table 3: Evaluation Results with Different Frame Input.

Figure 7: Dynamic Gaussian Rendering Visualization. The rendered dynamic vehicles exhibit accurate motion and align closely with their counterparts in the ground truth images. Note that we have enhanced the exposure of the rendered images for visualization purposes to facilitate comparative observation.

Figure 8: Visual Comparisons of Scene Reconstruction and Novel-View Synthesis on Scene-0098 (from 14 Selected Validation Scenes). Consistent with prior results, our ReconDrive maintains high-quality rendering in both scene reconstruction (Original View) and novel-view synthesis (1–3m lateral movement) compared to per-scene optimization methods (Street Gaussians, PVG, DeformableGS, OminiRe).

Figure 9: Visual Comparisons of Scene Reconstruction and Novel-View Synthesis on Scene-0968 (from 14 Selected Validation Scenes). Consistent with prior results, our ReconDrive maintains high-quality rendering in both scene reconstruction (Original View) and novel-view synthesis (1–3m lateral movement) compared to per-scene optimization methods (Street Gaussians, PVG, DeformableGS, OminiRe). Against the feed-forward DrivingForward, it preserves precise visual consistency—clear in tree near the car —with more notable performance gains as movement increases, plus less distortion/blurriness, especially at image boundaries.

Figure 10: Visual Comparisons of Scene Reconstruction and Novel-View Synthesis on Scene-1065 (from 14 Selected Validation Scenes). Consistent with prior results, our ReconDrive maintains high-quality rendering in both scene reconstruction (Original View) and novel-view synthesis (1–3m lateral movement) compared to per-scene optimization methods (Street Gaussians, PVG, DeformableGS, OminiRe). Against the feed-forward DrivingForward (MF/SF modes), it preserves precise visual consistency. This visualization further demonstrates that ReconDrive performs exceptionally well in critical scenarios like nighttime environments.

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EVolSplat4D: Efficient Volume-based Gaussian Splatting for 4D Urban Scene Synthesis

Sheng Miao, Sijin Li, Pan Wang, Dongfeng Bai+4

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3D Gaussian Splatting Dynamic Scene Reconstruction Dynamic Novel View Synthesis Feed-Forward 3D Reconstruction

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Novel view synthesis (NVS) of static and dynamic urban scenes is essential for autonomous driving simulation, yet existing methods often struggle to balance reconstruction time with quality. While state-of-the-art neural radiance fields and 3D Gaussian Splatting approaches achieve photorealism, they often rely on time-consuming per-scene optimization. Conversely, emerging feed-forward methods frequently adopt per-pixel Gaussian representations, which lead to 3D inconsistencies when aggregating multi-view predictions in complex, dynamic environments. We propose EvolSplat4D, a feed-forward framework that moves beyond existing per-pixel paradigms by unifying volume-based and pixel-based Gaussian prediction across three specialized branches. For close-range static regions, we predict consistent geometry of 3D Gaussians over multiple frames directly from a 3D feature volume, complemented by a semantically-enhanced image-based rendering module for predicting their appearance. For dynamic actors, we utilize object-centric canonical spaces and a motion-adjusted rendering module to aggregate temporal features, ensuring stable 4D reconstruction despite noisy motion priors. Far-Field scenery is handled by an efficient per-pixel Gaussian branch to ensure full-scene coverage. Experimental results on the KITTI-360, KITTI, Waymo, and PandaSet datasets show that EvolSplat4D reconstructs both static and dynamic environments with superior accuracy and consistency, outperforming both per-scene optimization and state-of-the-art feed-forward baselines.

Abstract

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