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DrivingGaussian: Composite Gaussian Splatting for Surrounding Dynamic Autonomous Driving Scenes

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arxiv 2312.07920 v3 pith:T5PH2JH7 submitted 2023-12-13 cs.CV

Xiaoyu Zhou , Zhiwei Lin , Xiaojun Shan , Yongtao Wang , Deqing Sun , Ming-Hsuan Yang This is my paper · ORCID
classification cs.CV
keywords drivinggaussiandynamicscenesdrivinggaussiansceneautonomouscomposite
verification ladder T0 review T1 audit T2 compute T3 formal
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We present DrivingGaussian, an efficient and effective framework for surrounding dynamic autonomous driving scenes. For complex scenes with moving objects, we first sequentially and progressively model the static background of the entire scene with incremental static 3D Gaussians. We then leverage a composite dynamic Gaussian graph to handle multiple moving objects, individually reconstructing each object and restoring their accurate positions and occlusion relationships within the scene. We further use a LiDAR prior for Gaussian Splatting to reconstruct scenes with greater details and maintain panoramic consistency. DrivingGaussian outperforms existing methods in dynamic driving scene reconstruction and enables photorealistic surround-view synthesis with high-fidelity and multi-camera consistency. Our project page is at: https://github.com/VDIGPKU/DrivingGaussian.

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Forward citations

Cited by 7 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. GS-Occ3D: Scaling Vision-only Occupancy Reconstruction with Gaussian Splatting

    cs.CV 2025-07 conditional novelty 6.0 of 10

    A camera-only Gaussian-surfel pipeline reconstructs full Waymo scenes, converts them to binary occupancy labels, and trains CVT-Occ to generalize on Occ3D-Waymo and Occ3D-nuScenes at a level close to or above LiDAR-la...

  2. Predicting 3D representations for Dynamic Scenes

    cs.CV 2025-01 conditional novelty 6.0 of 10

    A self-supervised model predicts future 3D radiance-field representations (triplanes) from monocular video and renders future views better than two adapted baselines on held-out scenes.

  3. GaussianPainter: Painting Point Cloud into 3D Gaussians with Normal Guidance

    cs.CV 2024-12 conditional novelty 6.0 of 10

    GaussianPainter produces 3D Gaussians from a point cloud and reference image in one forward pass by constraining Gaussian rotations with predicted surface normals.

  4. LiDAR-RT: Gaussian-based Ray Tracing for Dynamic LiDAR Re-simulation

    cs.CV 2024-12 conditional novelty 6.0 of 10

    LiDAR-RT re-simulates LiDAR views of dynamic driving scenes in real time by ray tracing Gaussian primitives with learnable intensity and ray-drop properties.

  5. DrivingRecon: Large 4D Gaussian Reconstruction Model For Autonomous Driving

    cs.CV 2024-12 conditional novelty 6.0 of 10

    DrivingRecon predicts 4D Gaussians of street scenes from surround-view video in one forward pass, using a novel Prune and Dilate Block to reduce redundant overlapping points.

  6. CRUISE: Cooperative Reconstruction and Editing in V2X Scenarios using Gaussian Splatting

    cs.CV 2025-07 conditional novelty 5.0 of 10

    CRUISE reconstructs real V2X driving scenes as editable Gaussians, then shows that training on its generated data improves 3D detection and tracking on the V2X-Seq benchmark.

  7. Bench2Drive-R: Turning Real World Data into Reactive Closed-Loop Autonomous Driving Benchmark by Generative Model

    cs.RO 2024-12 conditional novelty 5.0 of 10

    A reactive closed-loop driving simulator that uses a diffusion renderer with retrieval from real recordings, plus a nuPlan behavioral controller, to generate sensor images in response to an end-to-end driving model's actions.

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