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

TL;DR

EvolSplat4D is proposed, a feed-forward framework that moves beyond existing per-pixel paradigms by unifying volume-based and pixel-based Gaussian prediction across three specialized branches, and reconstructs both static and dynamic environments with superior accuracy and consistency.

Abstract

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.

Figures (18)

• 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 miao2025evolsplat 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 $\mathcal{D}_\text{dyn}$. The decoder regresses the 3D Gaussian attributes for each point, enabling the final rendering of the dynamic object's image and binary mask.
• Figure 5: Qualitative Comparison with feed-forward baselines on the KITTI-360 dataset.