Gaussian Splatting-Based Color and Shape Deformation Fields for Dynamic Scene Reconstruction

The 3DGS (3D Gaussian Splatting) series of works has achieved significant success in novel view synthesis, but further research is needed for dynamic scene reconstruction tasks. In this paper, we propose a new framework based on 3DGS for handling dynamic scene reconstruction problems involving color changes. Our approach employs a multi-stage training strategy combining motion and color deformation fields to accurately model dynamic geometry and appearance changes. Additionally, we design two modular components: the Dynamic Component for capturing motion variations and the Color Component for managing material and color changes. These components flexibly adapt to different scenes, enhancing our method’s versatility. Experimental results demonstrate that our method achieves real-time rendering at 80 FPS on an RTX 4090 and achieves higher reconstruction accuracy than baseline methods such as HexPlane and Deformable3DGS. Furthermore, it reduces training time by approximately 10%, indicating improved training efficiency. These quantitative results confirm the effectiveness of our approach in delivering high-fidelity 4D reconstruction of complex dynamic environments.