用3D高斯模型实现自动驾驶场景的逼真重建与自由编辑。
DrivingGaussian++: Towards Realistic Reconstruction and Editable Simulation for Surrounding Dynamic Driving Scenes
- 用增量3D高斯建模静态背景，动态物体用复合图结构表示。
- 结合激光雷达先验，重建精度优于现有方法，支持多视角合成。
- 无需训练即可编辑纹理、天气和物体运动，适合自动驾驶仿真。
我们提出DrivingGaussian++，一种高效且逼真的自动驾驶周围动态场景重建与可控编辑框架。该方法使用增量3D高斯建模静态背景，并通过复合动态高斯图重构移动物体，确保位置准确性和遮挡关系。结合激光雷达先验，实现细节丰富且一致的场景重建，在动态场景重建和逼真环视图像生成上优于现有方法。DrivingGaussian++支持无需训练的可控编辑，包括纹理修改、天气模拟和物体操作，利用多视角图像和深度先验。通过集成大语言模型（LLMs）与可控编辑，可自动生成动态物体运动轨迹，并在优化过程中提升真实感。实验表明，该方法在保持一致性的同时生成高度逼真的动态多视角驾驶场景，显著提升场景多样性。更多结果与代码见项目主页：https://xiong-creator.github.io/DrivingGaussian_plus.github.io
原文摘要 · Abstract (English)
We present DrivingGaussian++, an efficient and effective framework for realistic reconstructing and controllable editing of surrounding dynamic autonomous driving scenes. DrivingGaussian++ models the static background using incremental 3D Gaussians and reconstructs moving objects with a composite dynamic Gaussian graph, ensuring accurate positions and occlusions. By integrating a LiDAR prior, it achieves detailed and consistent scene reconstruction, outperforming existing methods in dynamic scene reconstruction and photorealistic surround-view synthesis. DrivingGaussian++ supports training-free controllable editing for dynamic driving scenes, including texture modification, weather simulation, and object manipulation, leveraging multi-view images and depth priors. By integrating large language models (LLMs) and controllable editing, our method can automatically generate dynamic object motion trajectories and enhance their realism during the optimization process. DrivingGaussian++ demonstrates consistent and realistic editing results and generates dynamic multi-view driving scenarios, while significantly enhancing scene diversity. More results and code can be found at the project site: https://xiong-creator.github.io/DrivingGaussian_plus.github.io
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