Towards a Multispectral RGB-IR-UV-D Vision System -- Seeing the Invisible in 3D
This work addresses the problem of limited spectral sensing in 3D vision for applications requiring detection of invisible features, though it appears incremental as it builds on existing multispectral and depth sensing methods.
The paper tackled the problem of capturing multispectral 3D data by developing a real-time sensing system that registers visible, infrared, and ultraviolet information into depth frames, enabling detection of hidden surface features. The result was demonstrated through case studies showing the system's capability to detect such features in 3D environments.
In this paper, we present the development of a sensing system with the capability to compute multispectral point clouds in real-time. The proposed multi-eye sensor system effectively registers information from the visible, (long-wave) infrared, and ultraviolet spectrum to its depth sensing frame, thus enabling to measure a wider range of surface features that are otherwise hidden to the naked eye. For that, we designed a new cross-calibration apparatus that produces consistent features which can be sensed by each of the cameras, therefore, acting as a multispectral "chessboard". The performance of the sensor is evaluated with two different cases of studies, where we show that the proposed system can detect "hidden" features of a 3D environment.