Spectral Multi-View Inverse Rendering

Chunyu Li, Yusuke Monno, Masatoshi Okutomi
Tokyo Institute of Technology
IEEE International Conference on Computational Photography (ICCP 2021)

The flowchart of Spectral MVIR.


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.

Presentation Video

Our Results

Here are the the estimated 3D shapes and the sRGB color representations converted from the estimated spectral reflectances.

The results under camera and LED bulb setup.

The results under camera and projector setup.


Spectral MVIR: Joint Reconstruction of 3D Shape and Spectral Reflectance

Chunyu Li, Yusuke Monno, Masatoshi Okutomi
IEEE International Conference on Computational Photography (ICCP 2021), May, 2021
● Paper [PDF]
● Supplementary Video [AVI]

マルチビューインバースレンダリングによる高精細な3D形状と分光反射率の同時推定 (Japanese)

李 淳雨, 紋野 雄介, 奥富 正敏
第28回画像センシングシンポジウム(SSII2022), June, 2022
● Poster [PDF]


Chunyu Li: lchunyu[at]ok.ctrl.titech.ac.jp
Yusuke Monno: ymonno[at]ok.sc.e.titech.ac.jp
Masatoshi Okutomi: mxo[at]sc.e.titech.ac.jp

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