无需标注即可精准渲染动态驾驶场景，实现自由视角生成。
AD-GS: Object-Aware B-Spline Gaussian Splatting for Self-Supervised Autonomous Driving
- 用局部贝塞尔曲线与全局三角函数结合建模物体运动轨迹。
- 自动分割物体与背景，用动态高斯和双向可见性掩码表示。
- 适合自动驾驶仿真，尤其在无标注数据下表现优异。
构建高质量动态城市驾驶场景的建模与渲染对自动驾驶仿真至关重要。现有高质量方法多依赖昂贵的手动物体轨迹标注，而自监督方法难以准确捕捉动态物体运动并合理分解场景，导致渲染伪影。本文提出 AD-GS，一种新颖的自监督框架，可从单个日志中实现高保真自由视角渲染。核心是可学习的运动模型，融合局部感知贝塞尔曲线与全局感知三角函数，实现灵活且精确的动态物体建模。无需完整语义标注，AD-GS通过简化伪2D分割自动将场景划分为物体与背景，使用动态高斯和双向时间可见性掩码表示物体。进一步引入可见性推理与物理刚性正则化以提升鲁棒性。大量实验表明，该无标注模型显著优于当前最先进的无标注方法，并具备与依赖标注方法相当的性能。
原文摘要 · Abstract (English)
Modeling and rendering dynamic urban driving scenes is crucial for self-driving simulation. Current high-quality methods typically rely on costly manual object tracklet annotations, while self-supervised approaches fail to capture dynamic object motions accurately and decompose scenes properly, resulting in rendering artifacts. We introduce AD-GS, a novel self-supervised framework for high-quality free-viewpoint rendering of driving scenes from a single log. At its core is a novel learnable motion model that integrates locality-aware B-spline curves with global-aware trigonometric functions, enabling flexible yet precise dynamic object modeling. Rather than requiring comprehensive semantic labeling, AD-GS automatically segments scenes into objects and background with the simplified pseudo 2D segmentation, representing objects using dynamic Gaussians and bidirectional temporal visibility masks. Further, our model incorporates visibility reasoning and physically rigid regularization to enhance robustness. Extensive evaluations demonstrate that our annotation-free model significantly outperforms current state-of-the-art annotation-free methods and is competitive with annotation-dependent approaches.
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