Spectral MVIR: Joint Reconstruction of 3D Shape and Spectral Reflectance
Chunyu Li, Yusuke Monno, and Masatoshi Okutomi

TL;DR
This paper introduces Spectral MVIR, a novel multi-view inverse rendering method that jointly reconstructs 3D shape and spectral reflectance from standard RGB images, enabling high-fidelity 3D models without specialized equipment.
Contribution
It presents a new rendering model considering spectral and geometric factors and develops a cost-optimization framework for joint 3D shape and spectral reflectance reconstruction.
Findings
Accurately reconstructs 3D shape and spectral reflectance from RGB images.
Does not require expensive equipment or complex calibration.
Works with synthetic and real-world data.
Abstract
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…
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Taxonomy
TopicsAdvanced Vision and Imaging · Computer Graphics and Visualization Techniques · Color Science and Applications
