UnMix-NeRF: Spectral Unmixing Meets Neural Radiance Fields
Authors: name: Fabian Perez, name: Sara Rojas, name: Carlos Hinojosa, name: Hoover Rueda-Chacón, name: Bernard Ghanem

Neural Radiance Field (NeRF)-based segmentation methods focus on object
semantics and rely solely on RGB data, lacking intrinsic material properties.
This limitation restricts accurate material perception, which is crucial for
robotics, augmented reality, simulation, and other applications. We introduce
UnMix-NeRF, a framework that integrates spectral unmixing into NeRF, enabling
joint hyperspectral novel view synthesis and unsupervised material
segmentation. Our method models spectral reflectance via diffuse and specular
components, where a learned dictionary of global endmembers represents pure
material signatures, and per-point abundances capture their distribution. For
material segmentation, we use spectral signature predictions along learned
endmembers, allowing unsupervised material clustering. Additionally, UnMix-NeRF
enables scene editing by modifying learned endmember dictionaries for flexible
material-based appearance manipulation. Extensive experiments validate our
approach, demonstrating superior spectral reconstruction and material
segmentation to existing methods. Project page:
https://www.factral.co/UnMix-NeRF.