  Reconstructing an object's high-quality 3D shape with inherent spectral
reflectance property, beyond typical device-dependent RGB albedos, opens the
door to applications requiring a high-fidelity 3D model in terms of both
geometry and photometry. In this paper, we propose a novel Multi-View Inverse
Rendering (MVIR) method called Spectral MVIR for jointly reconstructing the 3D
shape and the spectral reflectance for each point of object surfaces from
multi-view images captured using a standard RGB camera and low-cost lighting
equipment such as an LED bulb or an LED projector. Our main contributions are
twofold: (i) We present a rendering model that considers both geometric and
photometric principles in the image formation by explicitly considering camera
spectral sensitivity, light's spectral power distribution, and light source
positions. (ii) Based on the derived model, we build a cost-optimization MVIR
framework for the joint reconstruction of the 3D shape and the per-vertex
spectral reflectance while estimating the light source positions and the
shadows. Different from most existing spectral-3D acquisition methods, our
method does not require expensive special equipment and cumbersome geometric
calibration. Experimental results using both synthetic and real-world data
demonstrate that our Spectral MVIR can acquire a high-quality 3D model with
accurate spectral reflectance property.
