无需标注数据，实现城市街景动态物体的精准重建与编辑
DIAL-GS: Dynamic Instance Aware Reconstruction for Label-free Street Scenes with 4D Gaussian Splatting
- 通过外观位置不一致性识别动态物体实例
- 用实例感知的4D高斯点云实现动态自适应重建
- 适合自动驾驶场景建模与精细编辑，无需人工标注
城市场景重建对自动驾驶至关重要，可为数据生成和闭环测试提供结构化3D表示。监督方法依赖昂贵的人工标注且难以扩展，而现有自监督方法常混淆静态与动态元素，无法区分个体动态物体，限制了细粒度编辑。本文提出DIAL-GS，一种基于4D高斯点绘的无标签街景动态实例感知重建方法。首先利用扭曲渲染与实际观测之间的外观-位置不一致性准确识别动态实例；在实例级动态感知引导下，采用实例感知的4D高斯作为统一体素表示，实现动态自适应与实例感知重建。进一步引入互增强机制，使身份与动态特性相互强化，提升重建完整性与一致性。在城市驾驶场景实验中，DIAL-GS在重建质量与实例级编辑性能上均优于现有自监督基线，为城市场景建模提供了简洁而强大的解决方案。
原文摘要 · Abstract (English)
Urban scene reconstruction is critical for autonomous driving, enabling structured 3D representations for data synthesis and closed-loop testing. Supervised approaches rely on costly human annotations and lack scalability, while current self-supervised methods often confuse static and dynamic elements and fail to distinguish individual dynamic objects, limiting fine-grained editing. We propose DIAL-GS, a novel dynamic instance-aware reconstruction method for label-free street scenes with 4D Gaussian Splatting. We first accurately identify dynamic instances by exploiting appearance-position inconsistency between warped rendering and actual observation. Guided by instance-level dynamic perception, we employ instance-aware 4D Gaussians as the unified volumetric representation, realizing dynamic-adaptive and instance-aware reconstruction. Furthermore, we introduce a reciprocal mechanism through which identity and dynamics reinforce each other, enhancing both integrity and consistency. Experiments on urban driving scenarios show that DIAL-GS surpasses existing self-supervised baselines in reconstruction quality and instance-level editing, offering a concise yet powerful solution for urban scene modeling.
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