# Gaussian-UDSR: Real-Time Unbounded Dynamic Scene Reconstruction with 3D Gaussian Splitting
## 2 Related Work  
Novel view synthesis is a crucial technique in computer vision and autonomous driving. In recent years, some works [13; 14; 15; 16; 17; 18] have reconstructed objects from multi-view images and LiDAR inputs. However, these methods are limited to existing images, cannot present new views, and need help processing high-resolution images, which usually produce a noisy appearance. Neural Radiance Fields (NeRF) [19] has emerged as an approach of interest. NeRF employs coordinate-based multilayer perception of a 3D scene to predict the optical properties of the point in the scene and utilizes volume rendering and spatial smoothing of multilayer perception to generate high-quality novel views. However, its implicit nature also brings drawbacks, including slow training and rendering speeds and high memory consumption limited to simplified material and lighting models. Several studies have proposed solutions to these challenges to increase the training speed. Many NeRF-based methods [20; 21; 22; 23; 24] have achieved expressive compositing quality and good view consistency [25; 26; 27; 28; 29; 30]. Instant-NGP [21] introduces multiresolution hash coding, allowing smaller networks to reduce training costs. Depth-supervised NeRF [25] utilizes depth maps for fast training with few viewpoints. Sparsenerf [30] improves the ability to synthesize new viewpoints from sparse viewpoint data by ordering the depth of information. Recently, 3D Gaussian splatting for real-time radiance field rendering (3 DGS) [31] has brought about a technological change by fundamentally addressing these pain points of NeRF. 3 DGS expresses the scene as a displayed point cloud, retaining the high-quality benefits of volume rendering through a highly parallelized differentiable rasterization pipeline while significantly improving the training speed and rendering efficiency and providing control over the scene. However, 3 DGS targets static scenes and cannot model dynamic moving objects. Therefore, some researchers have tried to introduce 3 DGS techniques into the reconstruction of dynamic scenes 4 DGS [32] and Control4D [33] encode the spatial features of the scene using Tri-Plane [34] and HexPlane [35], respectively, and decode them using multilayer perceptron (MLP) to obtain the motion changes of each Gaussian point. D3 DGS [36] and GauFRe [37] directly utilize an unbounded MLP to predict the motion of each Gaussian point, and GauFRe also separates the Gaussian points statically and dynamically to distinguish between the static and dynamic parts of the field. SC-GS [38] introduces a set of control points to the scene and utilizes the MLP to predict the variation field of each control point. After that, the K-nearest neighbor (KNN) algorithm is used to find the neighboring control points of each Gaussian point and interpolate the neighboring control points' variation field to obtain the Gaussian point's motion variation. However, they are models constructed on small-scale data with low image quality and rendering speed. Therefore, this paper proposes a new 3D Gaussian framework-based unbounded dynamic scene reconstruction method, Gaussian-UDSR, for large-scale unbounded dynamic scenes focused on self-driving cars. Table 1 summarizes the representative dynamic scene reconstruction approaches based on NeRF and 3 DGS, comparing them across input modality, scene scale, dynamic modeling capability, rendering speed, and quality. While methods like SC-GS and GauFRe show promise in small-scale scenes, they are limited in rendering resolution and real-time applicability. In contrast, our proposed Gaussian-UDSR targets large-scale unbounded dynamic scenes with improved motion separation and real-time rendering, tailored for autonomous driving scenarios.