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DrivingRecon: Large 4D Gaussian Reconstruction Model For Autonomous Driving

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arxiv 2412.09043 v1 pith:5KAQB3OK submitted 2024-12-12 cs.CV

Hao Lu , Tianshuo Xu , Wenzhao Zheng , Yunpeng Zhang , Wei Zhan , Dalong Du , Masayoshi Tomizuka , Kurt Keutzer
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Yingcong Chen
This is my paper · ORCID
classification cs.CV
keywords reconstructiondrivingrecongaussianmodeldrivingsceneapplicationsautonomous
verification ladder T0 review T1 audit T2 compute T3 formal
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Photorealistic 4D reconstruction of street scenes is essential for developing real-world simulators in autonomous driving. However, most existing methods perform this task offline and rely on time-consuming iterative processes, limiting their practical applications. To this end, we introduce the Large 4D Gaussian Reconstruction Model (DrivingRecon), a generalizable driving scene reconstruction model, which directly predicts 4D Gaussian from surround view videos. To better integrate the surround-view images, the Prune and Dilate Block (PD-Block) is proposed to eliminate overlapping Gaussian points between adjacent views and remove redundant background points. To enhance cross-temporal information, dynamic and static decoupling is tailored to better learn geometry and motion features. Experimental results demonstrate that DrivingRecon significantly improves scene reconstruction quality and novel view synthesis compared to existing methods. Furthermore, we explore applications of DrivingRecon in model pre-training, vehicle adaptation, and scene editing. Our code is available at https://github.com/EnVision-Research/DriveRecon.

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Forward citations

Cited by 5 Pith papers

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  1. Targeted Structure Completion for Sparse-View 3D Reconstruction in Autonomous Driving

    cs.CV 2026-07 conditional novelty 6.0 of 10

    FocusGS localizes a 3D geometric ambiguity manifold from depth discontinuities and instantiates continuous Gaussian queries only there, yielding SOTA sparse-view driving reconstruction with far fewer Gaussians.

  2. 3D and 4D World Modeling: A Survey

    cs.CV 2025-09 conditional novelty 5.0 of 10

    A survey that defines 3D/4D world modeling, organizes methods into VideoGen, OccGen, and LiDARGen categories, and compiles datasets, metrics, and benchmark numbers.

  3. SceneCompleter: Dense 3D Scene Completion for Generative Novel View Synthesis

    cs.CV 2025-06 conditional novelty 5.0 of 10

    SceneCompleter jointly denoises RGB and depth latents, conditioned on projected depth and global scene features, yielding higher quality and more pose-consistent novel views than 2D-inpainting baselines.

  4. Advances in 4D Representation: Geometry, Motion, and Interaction

    cs.CV 2025-10 conditional novelty 4.0 of 10

    A representation-centric survey of 4D generation and reconstruction, organized by geometry, motion, and interaction, with qualitative trade-off comparisons across seven representation families.

  5. DriveGen3D: Boosting Feed-Forward Driving Scene Generation with Efficient Video Diffusion

    cs.CV 2025-10 conditional novelty 4.0 of 10

    DriveGen3D makes long driving-video synthesis and 3D scene reconstruction practical by caching only the conditional diffusion branch, quantizing cross-view attention, and fusing temporal context into a feed-forward Ga...

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