MSGS: Multispectral 3D Gaussian Splatting

MSGS addresses a specific technical gap in the 3D Gaussian Splatting (3DGS) ecosystem: the inability to handle non-RGB spectral data effectively. While standard 3DGS is optimized for human visual perception (RGB), MSGS targets domains like precision agriculture, satellite imaging, and medical diagnostics where hyperspectral or multispectral data is critical. The project scores a 4 on defensibility because, while it introduces a specialized optimization scheme (dual-loss) and per-band spherical harmonics, it remains an extension of a rapidly evolving base technology (3DGS). The barrier to entry for a competent graphics researcher to replicate this is relatively low, though the domain-specific knowledge of spectral-to-RGB conversion at the pixel level provides some niche protection. Frontier risk is low because entities like OpenAI or Google are focusing on general-purpose generative models rather than specialized radiometric reconstruction tools. The primary threat comes from the academic community and specialized vision startups (e.g., those in the remote sensing space) who may integrate similar multispectral capabilities into broader frameworks like NerfStudio. With 0 stars and 5 forks only 3 days after release, it shows early signs of academic interest (likely internal or related research groups) but has not yet achieved community traction. Its survival depends on becoming the reference implementation for multispectral 3DGS before larger frameworks absorb this capability as a standard plugin.