Photorealistic reconstruction of street scenes is essential for developing real-world simulators in autonomous driving. While recent methods based on 3D/4D Gaussian Splatting (GS) have demonstrated promising results, they still encounter challenges in complex street scenes due to the unpredictable motion of dynamic objects. Current methods typically decompose street scenes into static and dynamic objects, learning the Gaussians in either a supervised manner (e.g., w/ 3D bounding-box) or a self-supervised manner (e.g., w/o 3D bounding-box). However, these approaches do not effectively model the motions of dynamic objects (e.g., the motion speed of pedestrians is clearly different from that of vehicles), resulting in suboptimal scene decomposition. To address this, we propose Explicit Motion Decomposition (EMD), which models the motions of dynamic objects by introducing learnable motion embeddings to the Gaussians, enhancing the decomposition in street scenes. The proposed EMD is a plug-and-play approach applicable to various baseline methods. We also propose tailored training strategies to apply EMD to both supervised and self-supervised baselines. Through comprehensive experimentation, we illustrate the effectiveness of our approach with various established baselines. The code will be released at: this https URL.
街景的逼真重建对开发自动驾驶的真实场景模拟器至关重要。尽管基于 3D/4D 高斯投影（Gaussian Splatting, GS）的最新方法在该领域展现了良好前景，但由于动态物体不可预测的运动，这些方法在复杂街景中仍面临挑战。当前的方法通常将街景分解为静态和动态物体，并通过有监督（例如基于 3D 边界框）或自监督（例如无需 3D 边界框）的方式学习高斯分布。然而，这些方法未能有效建模动态物体的运动特性（例如，行人的运动速度明显不同于车辆），导致场景分解效果不够理想。 为了解决这一问题，我们提出了显式运动分解（Explicit Motion Decomposition, EMD）方法，通过向高斯分布中引入可学习的运动嵌入（motion embeddings），对动态物体的运动进行建模，从而增强街景的分解效果。所提出的 EMD 方法是一种可即插即用的方案，适用于多种基线方法。此外，我们还设计了针对性的训练策略，使 EMD 能够应用于有监督和自监督的基线方法。 通过全面的实验，我们验证了在多种基线方法中应用 EMD 的有效性，并表明其显著改善了街景动态物体的分解与建模。