Multispectral Gaussian Splatting for Novel View Rendering

Multispectral Gaussian Splatting (MGS) is a cutting-edge approach for novel view rendering, leveraging the power of Gaussian splatting to represent complex 3D scenes while incorporating multispectral data for enhanced fidelity and versatility. This project aims to develop an efficient MGS pipeline that synthesizes realistic novel views by modeling scenes as volumetric Gaussian distributions enriched with spectral information across multiple bands (e.g., RGB, infrared, or other wavelengths). By combining the benefits of Gaussian splatting with multispectral imaging, this method seeks to capture rich spatial and spectral details for applications such as advanced computer graphics, remote sensing, and medical imaging. Key challenges include managing the increased computational and memory demands introduced by multispectral data, optimizing Gaussian parameter computation (e.g., scale, rotation, covariance), and ensuring smooth blending of spectral features across views. The pipeline addresses these challenges through dynamic Gaussian culling, covariance matrix optimization, and efficient memory management using mixed precision operations and strategic CUDA memory handling. Additionally, advanced frustum culling and tiling strategies are employed to enhance scalability for large scenes. This approach significantly improves rendering quality for complex, multispectral scenes, enabling realistic novel views with enhanced spectral detail. The resulting framework not only advances the state-of-the-art in Gaussian-based rendering but also provides a robust foundation for multispectral applications in virtual reality, scientific visualization, and spectral image synthesis.