Spec-NeRF: Multi-spectral Neural Radiance Fields
Jiabao Li Yuqi Li Ciliang Sun Chong Wang Jinhui Xiang
We propose Multi-spectral Neural Radiance Fields(Spec-NeRF) for jointly
reconstructing a multispectral radiance field and spectral sensitivity
functions(SSFs) of the camera from a set of color images filtered by different
filters. The proposed method focuses on modeling the physical imaging process,
and applies the estimated SSFs and radiance field to synthesize novel views of
multispectral scenes. In this method, the data acquisition requires only a
low-cost trichromatic camera and several off-the-shelf color filters, making it
more practical than using specialized 3D scanning and spectral imaging
equipment. Our experiments on both synthetic and real scenario datasets
demonstrate that utilizing filtered RGB images with learnable NeRF and SSFs can
achieve high fidelity and promising spectral reconstruction while retaining the
inherent capability of NeRF to comprehend geometric structures. Code is
available at https://github.com/CPREgroup/SpecNeRF-v2.