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

• URL: http://arxiv.org/abs/2603.07552v1
• Date: Sun, 08 Mar 2026 09:31:15 GMT
• Title: ReconDrive: Fast Feed-Forward 4D Gaussian Splatting for Autonomous Driving Scene Reconstruction
• Abstract summary: ReconDrive is a feed-forward framework that leverages and extends the 3D foundation model VGGT for rapid, high-fidelity 4DGS generation.<n> Benchmarked on nuScenes, ReconDrive significantly outperforms existing feed-forward baselines in reconstruction, novel-view synthesis, and 3D perception.
• Score: 32.767300244488645
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• MotionScale: Reconstructing Appearance, Geometry, and Motion of Dynamic Scenes with Scalable 4D Gaussian Splatting [54.859943475818234]
  MotionScale is a 4D Gaussian Splatting framework that scales efficiently to large scenes and extended sequences.<n>Experiments on challenging real-world benchmarks demonstrate that MotionScale significantly outperforms state-of-the-art methods in both reconstruction quality and temporal stability.
  arXiv  Detail & Related papers  (2026-03-31T06:03:59Z)
• EVolSplat4D: Efficient Volume-based Gaussian Splatting for 4D Urban Scene Synthesis [43.898895514609286]
  EvolSplat4D is a feed-forward framework that moves beyond existing per-pixel paradigms by unifying volume-based and pixel-based Gaussian prediction.<n>We 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.
  arXiv  Detail & Related papers  (2026-01-22T13:39:29Z)
• Motion4D: Learning 3D-Consistent Motion and Semantics for 4D Scene Understanding [54.859943475818234]
  We present Motion4D, a novel framework that integrates 2D priors from foundation models into a unified 4D Gaussian Splatting representation.<n>Our method features a two-part iterative optimization framework: 1) Sequential optimization, which updates motion and semantic fields in consecutive stages to maintain local consistency, and 2) Global optimization, which jointly refines all attributes for long-term coherence.<n>Our method significantly outperforms both 2D foundation models and existing 3D-based approaches across diverse scene understanding tasks, including point-based tracking, video object segmentation, and novel view synthesis.
  arXiv  Detail & Related papers  (2025-12-03T09:32:56Z)
• DGGT: Feedforward 4D Reconstruction of Dynamic Driving Scenes using Unposed Images [36.562825380568384]
  We introduce textbfDriving Gaussian Grounded Transformer (DGGT), a unified framework for pose-free dynamic scene reconstruction.<n>Our approach jointly predicts per-frame 3D Gaussian maps and camera parameters, disentangles dynamics with a lightweight dynamic head.<n>A diffusion-based rendering refinement further reduces motion/interpolation artifacts and improves novel-view quality under sparse inputs.
  arXiv  Detail & Related papers  (2025-12-02T18:29:18Z)
• 4D3R: Motion-Aware Neural Reconstruction and Rendering of Dynamic Scenes from Monocular Videos [52.89084603734664]
  We present 4D3R, a pose-free dynamic neural rendering framework that decouples static and dynamic components through a two-stage approach.<n>Our approach achieves up to 1.8dB PSNR improvement over state-of-the-art methods.
  arXiv  Detail & Related papers  (2025-11-07T13:25:50Z)
• 4DRadar-GS: Self-Supervised Dynamic Driving Scene Reconstruction with 4D Radar [15.713470339586058]
  We present a 4D Radar-augmented self-supervised 3D reconstruction framework tailored for dynamic driving scenes.<n>4DRadar-GS achieves state-of-the-art performance in dynamic driving scene 3D reconstruction.
  arXiv  Detail & Related papers  (2025-09-16T10:29:43Z)
• CoDa-4DGS: Dynamic Gaussian Splatting with Context and Deformation Awareness for Autonomous Driving [12.006435326659526]
  We introduce a novel 4D Gaussian Splatting (4DGS) approach to improve dynamic scene rendering.<n> Specifically, we employ a 2D semantic segmentation foundation model to self-supervise the 4D semantic features of Gaussians.<n>By aggregating and encoding both semantic and temporal deformation features, each Gaussian is equipped with cues for potential deformation compensation.
  arXiv  Detail & Related papers  (2025-03-09T19:58:51Z)
• OG-Gaussian: Occupancy Based Street Gaussians for Autonomous Driving [12.47557991785691]
  We propose OG-Gaussian, a novel approach that replaces LiDAR point clouds with Occupancy Grids (OGs) generated from surround-view camera images.<n>Our method leverages the semantic information in OGs to separate dynamic vehicles from static street background, converting these grids into two distinct sets of initial point clouds for reconstructing both static and dynamic objects.<n>Experiments on the Open dataset demonstrate that OG-Gaussian is on par with the current state-of-the-art in terms of reconstruction quality and rendering speed, achieving an average PSNR of 35.13 and a rendering speed of 143 FPS
  arXiv  Detail & Related papers  (2025-02-20T04:00:47Z)
• 4D Gaussian Splatting: Modeling Dynamic Scenes with Native 4D Primitives [115.67081491747943]
  Dynamic 3D scene representation and novel view synthesis are crucial for enabling AR/VR and metaverse applications.<n>We reformulate the reconstruction of a time-varying 3D scene as approximating its underlying 4D volume.<n>We derive several compact variants that effectively reduce the memory footprint to address its storage bottleneck.
  arXiv  Detail & Related papers  (2024-12-30T05:30:26Z)
• Driv3R: Learning Dense 4D Reconstruction for Autonomous Driving [116.10577967146762]
  We propose Driv3R, a framework that directly regresses per-frame point maps from multi-view image sequences.<n>We employ a 4D flow predictor to identify moving objects within the scene to direct our network focus more on reconstructing these dynamic regions.<n>Driv3R outperforms previous frameworks in 4D dynamic scene reconstruction, achieving 15x faster inference speed.
  arXiv  Detail & Related papers  (2024-12-09T18:58:03Z)
• Event-boosted Deformable 3D Gaussians for Dynamic Scene Reconstruction [50.873820265165975]
  We introduce the first approach combining event cameras, which capture high-temporal-resolution, continuous motion data, with deformable 3D-GS for dynamic scene reconstruction.<n>We propose a GS-Threshold Joint Modeling strategy, creating a mutually reinforcing process that greatly improves both 3D reconstruction and threshold modeling.<n>We contribute the first event-inclusive 4D benchmark with synthetic and real-world dynamic scenes, on which our method achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2024-11-25T08:23:38Z)
• EmerNeRF: Emergent Spatial-Temporal Scene Decomposition via Self-Supervision [85.17951804790515]
  EmerNeRF is a simple yet powerful approach for learning spatial-temporal representations of dynamic driving scenes. It simultaneously captures scene geometry, appearance, motion, and semantics via self-bootstrapping. Our method achieves state-of-the-art performance in sensor simulation.
  arXiv  Detail & Related papers  (2023-11-03T17:59:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.