# Gaussian-UDSR: Real-Time Unbounded Dynamic Scene Reconstruction with 3D Gaussian Splitting
## 1 Introduction  
Boundless dynamic scene reconstruction is crucial in autonomous driving, high-precision mapping, and virtual reality. For example, by reconstructing dynamic environments, autonomous driving systems can more accurately understand the structure and composition of their surroundings and distinguish between static objects and dynamic objects to make more effective path planning and decision-making. These capabilities require us to reconstruct 3D scenes from captured environmental information efficiently and render high-quality novel views in real time, which remains a challenge in unbounded dynamic environments. Scene representations based on Neural Radiance Fields [1, 2, 3, 4, 5, 6, 7], among others, have been extensively explored in the existing literature. S-NeRF [4] improves 3D reconstruction instreet view with an optimized network structure. However, it is sensitive to low-quality images and illumination variations, leading to possible errors in the 3D models generated in complex environments. Streetsur [5] applies a multi-view implicit surface reconstruction technique to street view to generate accurate 3D reconstruction by extending it to street view while dealing with the complexity of moving objects and dynamic environments. However, the accuracy may be limited due to the low quality of street-view images and the influence of complex illumination and occlusion. There may be computational efficiency problems for the processing of unbounded scenes, and there still needs improvement in the processing of dynamic objects, especially in real time. Moreover, this is a crucial aspect in simulating autonomous driving environments. Recently, several approaches [8; 9; 10; 11] propose to represent a dynamic scene as a synthetic neural representation consisting of a moving car in the foreground and a static background. The NSG [9] approach learns the object and motion relationships in the scene graph through neural networks, encodes spatial relationships between different objects, and efficiently supports the modeling and tracking of dynamic objects, which is not only able to perform dynamic scene modeling, but also capture the interactions between objects. interactions. However, the problem of computational complexity is still faced in unbounded and complex scenes, and the graph structure may lead to time and space efficiency problems. Panoptic Neural Fields (PNF) [10], which combines semantic segmentation with a neural field representation, enables the model to understand and generate scenes containing multiple semantic objects. By layering the objects in the scene, PNF provides better object-background separation, leading to more accurate modeling of dynamic and static objects. Despite providing better modeling for static objects, PNF still has limited performance for dynamic objects in the scene, especially fast-moving objects. In this work, we propose Gaussian-UDSR, a novel representation framework designed to tackle the core challenge of real-time reconstruction and rendering of unbounded dynamic scenes, which is essential for autonomous driving and other large-scale dynamic environments. Our key idea is to adopt 3D Gaussian splatting as a unified representation to jointly model static backgrounds and dynamic foreground objects. This representation is lightweight, differentiable, and well-suited for real-time processing. It also integrates LiDAR and SfM point cloud data, leveraging the precise geometry from LiDAR and dense texture information from SfM, thus enhancing scene reconstruction quality in unbounded outdoor environments. Our second contribution is the introduction of a Gaussian feature prediction network, which replaces traditional spherical harmonic basis functions with a learnable module. This network effectively captures both global contextual information and local object-specific features, enabling robust appearance modeling under varying lighting conditions and occlusions. Finally, we conduct comprehensive experiments on the Waymo and KITTI datasets.