Feedforward reconstruction is crucial for autonomous driving applications, where rapid scene reconstruction enables efficient utilization of large-scale driving datasets in closed-loop simulation and other downstream tasks, eliminating the need for time-consuming per-scene optimization. We present StreetForward, a pose-free and tracker-free feedforward framework for dynamic street reconstruction. Building upon the alternating attention mechanism from Visual Geometry Grounded Transformer (VGGT), we propose a simple yet effective temporal mask attention module that captures dynamic motion information from image sequences and produces motion-aware latent representations. Static content and dynamic instances are represented uniformly with 3D Gaussian Splatting, and are optimized jointly by cross-frame rendering with spatio-temporal consistency, allowing the model to infer per-pixel velocities and produce high-fidelity novel views at new poses and times. We train and evaluate our model on the Waymo Open Dataset, demonstrating superior performance on novel view synthesis and depth estimation compared to existing methods. Furthermore, zero-shot inference on CARLA and other datasets validates the generalization capability of our approach. More visualizations are available on our project page: https://streetforward.github.io.
Figures & tables
Figure 1. S t r e e t F o r w a r d : High-fidelity spatio-temporal extrapolated view synthesis via feedforward 3DGS dynamic street reconstruction. The right pane illustrates our 3DGS representation of a dynamic street with precise velocities, enabling motion-aware rendering at novel poses and times independent of segmentation or tracking.
SfM
Feedforward
Capability
StreetGS ( Yan et al., 2024 )
OmniRe ( Chen et al., )
VGGT ( Wang et al., 2025a )
DVGT ( Zuo et al., 2025 )
DGGT ( Chen et al., 2025a )
STORM ( Yang et al., 2024a )
EVolSplat 4D ( Miao et al., 2026 )
Flux4D ( Wang et al., )
Ours
Novel View Synthesis (3DGS)
No camera poses needed
No per-scene optimization
No LiDAR needed
Models objects motions
Table 1. Dynamic 3D street reconstruction approaches. Approaches are grouped into Structure from Motion (SfM) and Feedforward. indicates the capability is supported while is not; “No tracker needed” means no instance-level tracker is required to synthesize views at novel timestamps. Our method is highlighted in the last column, which satisfies all listed capabilities.
Figure 2. The proposed S t r e e t F o r w a r d pipeline. We illustrate two common types of dynamic scenes with rigid objects(vehicles) and deformable objects(pedestrians) in and . The input video is first encoded into per-frame patchified features and then processed by L times alternating global- and frame-attention to aggregate information across frames. These aggregated features are directly decoded by a camera head, a depth head and a Gaussian Head to obtain poses, depth and Gaussian attributes. Then causal masked attention is introduce to form motion-aware features, which are used to estimate both forward and backward motion as well as dynamic mask for separating static and dynamic Gaussians. The final 4D scene is obtained by combining static Gaussians with dynamic Gaussians propagated across time using the predicted motion.
Figure 3. Robustness to false-positive motion-mask prompts. Our method correctly assigns near-zero dynamic probability to parked or slowly moving vehicles, even if they are labeled as dynamic in annotations. This accurate understanding of motion (c) eliminates the motion ambiguity seen in DGGT (a), resulting in stable and clear rendering of the target vehicle over time (d).
Figure 4. Enforcing rigid regularization ( Lrigid ) removes the structural floaters seen around rigid objects in (a).
Figure 5. Temporal interpolation. With observation at time t-1 and t+1, we manage to correctly synthesize at time t. The figure compares the results of our full model against an ablation without backward fusion by predicting forward velocity only, and DGGT ( Chen et al., 2025a ) . The left three columns show pedestrian is with correct poses, and the right three columns display vehicle geometry reconstruction, where our model delivers complete geometries.
Figure 6. Qualitative comparision on Waymo Open Datasets.
High-fidelity reconstruction of driving scenes is crucial for autonomous driving. While recent feedforward 3D Gaussian Splatting (3DGS) methods enable fast reconstruction, their per-pixel Gaussian prediction paradigm often suffers from multi-view inconsistency and layering artifacts. Moreover, existing methods often model dynamic instances via dense flow prediction, which lacks explicit cross-view correspondence and instance-level consistency. In this paper, we propose PointForward, a feedforward driving reconstruction framework through point-aligned representations. Unlike pixel-aligned methods, we initialize sparse 3D queries in world space and aggregate multi-view image information via spatial-temporal fusion onto these queries, enforcing explicit cross-view consistency in a single feedforward pass. To handle scene dynamics, we introduce scene graphs that explicitly organize moving instances during reconstruction. By leveraging 3D bounding boxes, our method enables instance-level motion propagation and temporally consistent dynamic representations. Extensive experiments demonstrate that PointForward achieves state-of-the-art performance on large-scale driving benchmarks. The code will be available upon the publication of the paper.
Cheng Chi, Xianqi Wang, Hongcheng Luo +9
Xiaomi EV · Huazhong University of Science and Technology · Zhejiang University
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.
Haibao Yu, Kuntao Xiao, Jiahang Wang +7
The University of Hong Kong · Tuojing Intelligence · King’s College London +2
Dynamic scene reconstruction in autonomous driving remains a fundamental challenge due to significant temporal variations, moving objects, and complex scene dynamics. Existing feed-forward 3D models have demonstrated strong performance in static reconstruction but still struggle to capture dynamic motion. To address these limitations, we propose DynamicVGGT, a unified feed-forward framework that extends VGGT from static 3D perception to dynamic 4D reconstruction. Our goal is to model point motion within feed-forward 3D models in a dynamic and temporally coherent manner. To this end, we jointly predict the current and future point maps within a shared reference coordinate system, allowing the model to implicitly learn dynamic point representations through temporal correspondence. To efficiently capture temporal dependencies, we introduce a Motion-aware Temporal Attention (MTA) module that learns motion continuity. Furthermore, we design a Dynamic 3D Gaussian Splatting Head that explicitly models point motion by predicting Gaussian velocities using learnable motion tokens under scene flow supervision. It refines dynamic geometry through continuous 3D Gaussian optimization. Extensive experiments on autonomous driving datasets demonstrate that DynamicVGGT significantly outperforms existing methods in reconstruction accuracy, achieving robust feed-forward 4D dynamic scene reconstruction under complex driving scenarios.
Zhuolin He, Jing Li, Guanghao Li +9
Fudan University · Yinwang Intelligent Technology · Huawei +2