Yes, the Textured-GS approach could be effectively combined with other scene representation techniques, such as neural radiance fields (NeRF), to leverage the strengths of both methods. By integrating the spatially defined color and opacity variations of textured Gaussians with the implicit representation capabilities of NeRF, a hybrid model could be developed that benefits from the high-quality rendering of complex scenes while maintaining efficient computational performance.
For instance, the Gaussian splatting technique could be used to represent the overall structure and geometry of the scene, while NeRF could be employed to capture fine details and intricate lighting effects. This combination would allow for the efficient rendering of large-scale environments, where the textured Gaussians provide a robust framework for handling occlusions and depth, while NeRF enhances the visual quality through its ability to model view-dependent effects.
Additionally, the integration could involve using the output of NeRF as a guide for optimizing the parameters of the textured Gaussians, ensuring that the two methods complement each other. This synergy could lead to improved rendering quality, faster convergence during training, and a more versatile framework capable of handling a wider range of scene complexities and lighting conditions.