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

Sheng Miao; Sijin Li; Pan Wang; Dongfeng Bai; Bingbing Liu; Yue Wang; Andreas Geiger; Yiyi Liao

Figure 1 : Overview of EVolSplat4D . We propose EVolSplat4D, a unified feed-forward 3D Gaussian Splatting framework tailored for static & dynamic urban scenes that achieves real-time rendering speeds. Leveraging both camera and tracked 3D bounding box as inputs, EVolSplat4D completes scene reconstruction in approximately 1.3 seconds, achieving photo-realistic quality comparable to time-consuming per-scene optimization methods. EVolSplat4D also supports various downstream applications, including high-fidelity scene editing and scene decomposition.

Figure 2 : Method Overview. We reconstruct urban scenes by disentangling them as close-range volume, dynamic actors, and far-field scenery, predicting 3D Gaussians of each in a feed-forward manner. a) Given a set of images, we initialize our model with the pretrained depth model and DINO feature extractor. b) In close-range volume, we leverage the 3D context of

\mathcal{F}^{\text{3D}}

to predict the geometry attributes of 3D Gaussians and project the 3D Gaussians to the reference views to retrieve 2D context, including color window and visibility maps to decode their color. c) For dynamic actors, we model each instance using an instance-wise canonical space and perform feed-forward reconstitution through our proposed motion-adjusted IBR module. d) To model far-range regions, we employ a 2D U-Net backbone

\mathcal{F}^{\text{2D}}

with cross-view self-attention to aggregate information from nearby reference images and predict per-pixel Gaussians. e) The composition of the three parts leads to our full model for unbounded scenes.

Figure 3 : Occlusion Illustration . a) One Gaussian in 3D space may retrieve inaccurate color information from 2D reference images due to occlusions. b) The previous method ( Miao et al., 2025 ) uses depth priors to check occlusions, which may suffer from inaccurate monocular depth predictions. c) In contrast, EVolSplat4D comprises robust DINO priors to reduce the impact of invisible colors to enhance rendering quality.

Figure 4 : Motion Adjusted IBR. LiDAR points in the canonical space for an instance

m

are transformed using time-specific poses and projected into reference frames. A Window-based Projection strategy then samples coherent appearance features

c_{ik}

and visibility map

v_{ik}

, which are input to the dynamic Gaussian decoder

D_{\text{dyn}}

. The decoder regresses the 3D Gaussian attributes for each point, enabling the final rendering of the dynamic object’s image and binary mask.

Cat.	Method	Sensors	3D Box Tracks	Rep.	Feed-Forward	Dynamic	Real-Time	Scene Edit
Per-scene Opt.	SUDS ( Turki et al., 2023 )	LiDAR+RGB	N	Non-PA	✘	✔	✘	✘
	EmerNeRF ( Yang et al., 2023a )	LiDAR+RGB	N	Non-PA	✘	✔	✘	✘
	StreetGaussian ( Yan et al., 2024 )	LiDAR+RGB	Pred	Non-PA	✘	✔	✔	✔
	OmniRe ( Chen et al., 2024c )	LiDAR+RGB	GT	Non-PA	✘	✔	✔	✔
	DeSiRe-GS ( Peng et al., 2025 )	LiDAR+RGB	N	Non-PA	✘	✔	✔	✘
FeedForward Recon.	AnySplat ( Jiang et al., 2025a )	RGB	N	PA	✔	✘	✔	✘

Table 1 : Categorization of representative baselines. We categorize representative methods into per-scene optimization and feed-forward reconstruction methods. PA and Non-PA denote pixel-aligned and non-pixel-aligned representations, respectively.

Method	KITTI- $360$ (In-Domain)			Waymo (Out-of-Domain)			FPS $\uparrow$	Mem.(GB) $\downarrow$
Method	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	(Render res.: 376 $\times$ 1408)
MVSNeRF ( Chen et al., 2021 )	18.44	0.638	0.317	17.86	0.595	0.433	0.025	12.03
MuRF ( Xu et al., 2023 )	22.77	0.780	0.229	23.33	0.770	0.269	0.31	26.45
EDUS ( Miao et al., 2024 )	22.13	0.745	0.178	23.18	0.745	0.164	0.14	5.75
PixelSplat ( Charatan et al., 2024 )	19.41	0.584	0.357	16.65	0.541	0.579	81.58	26.75
MVSplat ( Chen et al., 2024b )	21.22	0.695	0.268	21.33	0.665	0.308	70.46	16.14

Table 2 : Quantitative results on Static Scenes with other feed-forward baselines on both KITTI-360 and Waymo open datasets. All models are trained on the KITTI-360 dataset using a drop50% sparsity level. Metrics are averaged on five validation scenes without any finetuning. It is also worth noting that our method is more memory efficient compared with other 3DGS-based methods.

Figure 5 : Qualitative Comparison with feed-forward baselines on the KITTI-360 dataset.

Method	KITTI (In-Domain)			Waymo (In-Domain)			PandaSet (Out-of-Domain)			FPS $\uparrow$	Mem.(GB) $\downarrow$
Method	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	(Render res.: 320 $\times$ 480 )
DrivingRecon ( Lu et al., 2024 )	19.50	0.597	0.283	20.21	0.572	0.277	14.92	0.506	0.389	190.78	9.48
STORM ( Yang et al., 2024a )	18.96	0.658	0.256	23.76	0.764	0.182	23.13	0.766	0.207	178.57	22.31
EVolSplat	16.52	0.583	0.275	22.86	0.753	0.201	23.32	0.746	0.253	212.42	6.41
EVolSplat4D	20.76	0.738	0.162	26.32	0.822	0.121	26.27	0.823	0.132	201.06	6.71

Table 3 : Quantitative results on Dynamic Scenes with feed-forward baselines. Metrics are averaged on five validation scenes without any finetuning in drop80% sparsity level. Our EVolSplat4D generalizes better than all the baseline in a large margin on both in-domain (KITTI, Waymo) and out-of-domain (PandaSet) datasets.

Figure 6 : Qualitative Comparisons with baselines for feed-forward inference on the KITTI dataset. The rendering images are downscaled 2x to 188

\times

621.

Figure 7 : Qualitative Comparisons with baselines for feed-forward inference on the Waymo Open dataset and PandaSet. Each column represents a different method, while each pair of rows corresponds to a different dataset. The rendering images resolution are 480

\times

320 for Waymo and 480

\times

270 for PandaSet.

Method	KITTI	Waymo	PandaSet
DriveRecon ( Lu et al., 2024 )	0.350	0.315	0.383
STORM ( Yang et al., 2024a )	0.104	0.080	0.102
EVolSplat4D (Ours)	0.062	0.063	0.080

Table 4 : Quantitative Comparison of View Extrapolation Quality . We report averaged KID

\downarrow

(lower values indicate better performance) on diverse datasets without finetuning in Drop 80% sparsity level.

Figure 8 : Extrapolated Views Qualitative Comparison with STORM on PandaSet.

Figure 9 : Convergence Analysis on the Waymo Dataset. We plot PSNR and LPIPS on one test set at different training steps. Compared with optimization-based baselines, our method with generalizable priors converges faster (

\approx

1000 steps) and achieves better PSNR.

Method	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	Rec. Time $\downarrow$
SUDS ( Turki et al., 2023 )	25.03	0.813	0.167	45min
EmerNeRF ( Yang et al., 2023a )	27.03	0.886	0.069	21min
StreetGaussian ( Yan et al., 2024 )	27.27	0.891	0.057	7min
DeSiRe-GS ( Peng et al., 2025 )	27.35	0.889	0.094	14min
OmniRe ( Chen et al., 2024c )	28.16	0.912	0.051	9min
EVolSplat4D (Ours ${}_{\text{0-iter}}$ )	25.39	0.841	0.119	1.3s

Table 5 : Comparison with Optimization-Based baselines on 3 sequences of the Waymo NOTR dataset under the Drop 80% sparsity level. The Rec. Time refers to the reconstruction time needed to reach convergence and achieve the reported metrics.

Method	Waymo		PandaSet
Method	KID $\downarrow$ @ 1 m	KID $\downarrow$ @ 3 m	KID $\downarrow$ @ 1 m	KID $\downarrow$ @ 3 m
StreetGaussian ( Yan et al., 2024 )	0.055	0.203	0.062	0.198
OmniRe ( Chen et al., 2024c )	0.041	0.158	0.033	0.116
EVolSplat4D (Ours-FF)	0.072	0.230	0.095	0.244
EVolSplat4D (Ours-FT)	0.038	0.114	0.029	0.118

Table 6: Quantitative comparison of view extrapolation quality with optimization-based baselines. We report KID at 1 m and 3 m lane shifts on Waymo and PandaSet.

Figure 10 : Qualitative Results of Ablation Study. We represent rendering images via feed-forward inference on one novel scene.

	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$
w/o volume branch	27.02	0.839	0.132
w/o pixel branch	26.29	0.817	0.194
w/o motion-adjusted IBR	26.77	0.837	0.121
w/o Occ. check	27.33	0.844	0.113
w/o $\mathcal{L}_{\text{mask}}$	27.45	0.847	0.108
Full	27.78	0.856	0.102

Table 7: Ablation Analysis of method components.

Setting	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$
$W=1$ , Pred bbx	27.45	0.850	0.115
$W=3$ , Pred bbx	27.78	0.856	0.102
$W=5$ , Pred bbx	27.56	0.853	0.106
$W=3$ , Gt bbx	27.73	0.860	0.095

Table 8: Ablation study on window size (

W

) and bounding box source. Using

W=3

with predicted bounding boxes (Pred bbx) yields the best results among window sizes. Notably, this performance is comparable to using ground-truth bounding boxes (Gt bbx).

Figure 11 : Qualitative Results of Scene Decompose Loss. The images are rendered only from the Gaussians within the close-range region. Without

\mathcal{L}_{\text{mask}}

, the far-field pixel branch partially models the close-range building, causing Gaussians from the

\mathcal{G}^{\mathrm{cr}}

to be semi-transparent.

Noisy Setting	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$
Pred Box+Center noise $\sigma=0.25\,\mathrm{m}$	27.28	0.836	0.120
Pred Box+Center noise $\sigma=0.5\,\mathrm{m}$	26.76	0.821	0.125
Pred Box+Yaw noise $\sigma=10^{\circ}$	27.52	0.844	0.114
Pred Box+Yaw noise $\sigma=15^{\circ}$	27.31	0.841	0.117
Pred Box	27.78	0.856	0.102

Table 9 : Ablation study of bounding box perturbation on the Waymo dataset.

	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$
$i=0$	27.260	0.833	0.142
$i=1$	27.787	0.856	0.102
$i=2$	27.781	0.856	0.102
$i=3$	27.785	0.856	0.102

Table 10: Ablation Study on recursion of

\Delta\mu_{i}

. The results are tested on the Waymo dataset.

Modality	Method	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$
Monocular Pred.	UniDepth ( Piccinelli et al., 2024 )	27.59	0.854	0.105
Monocular Pred.	Metric3D ( Yin et al., 2023 )	27.86	0.859	0.104
LiDAR Compl.	Prior Depth Anything ( Wang et al., 2025e )	27.78	0.856	0.102

Table 11 : Depth Sensitivity Experiment. The results are averaged on five testsets from the Waymo dataset.

Figure 12 : Scene Decomposition on Waymo. Our approach enables clear decomposition of the close-range volume, dynamic actors, and the far-field scenery.

Figure 13 : Scene Editing on KITTI. Images in the first and second columns represent the results before and after editing.

SparseCNN Network Architecture
Layer	Description	In/Out Ch.
Conv 0	kernel = $3\times 3\times 3$ , stride = $1$	16/16
Conv 1	kernel = $3\times 3\times 3$ , stride = $2$	16/16
Conv 2	kernel = $3\times 3\times 3$ , stride = $2$	16/32
Conv 3	kernel = $3\times 3\times 3$ , stride = $1$	32/32
Conv 4	kernel = $3\times 3\times 3$ , stride = $2$	32/64

Table S1 : Architecture of SparseConvNet . Each layer consists of sparse convolution, batch normalization, and ReLU.

a) MuRF

Figure S2: Qualitative scene reconstruction comparisons with per-scene optimization baselines under the Drop80% sparsity level

Method	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	Rec. Time $\downarrow$
EmerNeRF ( Yang et al., 2023a )	26.70	0.863	0.125	43 min
StreetGaussian ( Yan et al., 2024 )	26.91	0.870	0.092	15 min
OmniRe ( Chen et al., 2024c )	27.35	0.884	0.087	21 min
EVolSplat4D (Ours-FT)	27.28	0.901	0.071	6 min 04 s

Table S2 : Comparison with optimization-based baselines on three 120-frame Waymo NOTR validation sequences under the denser Drop 25% sparsity setting and higher resolution (

640\times 960

). Rec. Time denotes the reconstruction time required to achieve the reported metrics.

Figure S3 : Efficiency scaling analysis. We report memory usage and inference time with respect to the number of input frames and image resolution.

Figure S4 : Downstream utility visualization, including novel RGB&Depth rendering, optical flow, dynamic mask, and re-rendering with modified camera parameters.

Figure S5 : Qualitative comparison of view extrapolation quality. We compare 1 m and 3 m lane-shifted rendering results with optimization-based baselines.

Figure S6 : Qualitative editing comparison with OmniRe on actor replacement and 2 m lane shift.

Figure S7 : Failure case for actor lane shift under a large translation.

Figure S8 : Qualitative results of bounding box perturbation on Waymo.

Figure S9 : More Qualitative Results on Waymo . We visualize synthesized images (odd rows) and the corresponding predicted dynamic actors (even rows) on novel scenes generated by our pretrained model through a feed-forward inference.

Figure S10 : More Qualitative Results on out-of-domain dataset PandaSet . We visualize synthesized images (odd rows) and the corresponding predicted dynamic actors (even rows) on novel scenes generated by our pretrained model through a feed-forward inference.

Figure S11 : More Qualitative Results on KITTI . We visualize synthesized images (odd rows) and the corresponding predicted dynamic actors (even rows) on novel scenes generated by our pretrained model through a feed-forward inference.

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

Abstract

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