Spectral 3D Computer Vision -- A Review

Spectral 3D computer vision examines both the geometric and spectral properties of objects. It provides a deeper understanding of an object's physical properties by providing information from narrow bands in various regions of the electromagnetic spectrum. Mapping the spectral information onto the 3D model reveals changes in the spectra-structure space or enhances 3D representations with properties such as reflectance, chromatic aberration, and varying defocus blur. This emerging paradigm advances traditional computer vision and opens new avenues of research in 3D structure, depth estimation, motion analysis, and more. It has found applications in areas such as smart agriculture, environment monitoring, building inspection, geological exploration, and digital cultural heritage records. This survey offers a comprehensive overview of spectral 3D computer vision, including a unified taxonomy of methods, key application areas, and future challenges and prospects

Continuous 3D Reconstruction of Plants with Multispectral Information

Phenotyping is the process of identifying desirables traits of plants. These traits do not depend only on the plant genome but also on the environment. Imaging techniques can be applied on this field to help relieving the bottleneck due to manual gathering technique. Climate changes represent a challenge to satisfy the increasing demand of food. Phenotyping can relieve this problem. In this work I developed a robust pipeline to build a 3D multispectral model of as a basis for phenotyping

Proximal hyperspectral imaging detects diurnal and drought-induced changes in maize physiology

Hyperspectral imaging is a promising tool for non-destructive phenotyping of plant physiological traits, which has been transferred from remote to proximal sensing applications, and from manual laboratory setups to automated plant phenotyping platforms. Due to the higher resolution in proximal sensing, illumination variation and plant geometry result in increased non-biological variation in plant spectra that may mask subtle biological differences. Here, a better understanding of spectral measurements for proximal sensing and their application to study drought, developmental and diurnal responses was acquired in a drought case study of maize grown in a greenhouse phenotyping platform with a hyperspectral imaging setup. The use of brightness classification to reduce the illumination-induced non-biological variation is demonstrated, and allowed the detection of diurnal, developmental and early drought-induced changes in maize reflectance and physiology. Diurnal changes in transpiration rate and vapor pressure deficit were significantly correlated with red and red-edge reflectance. Drought-induced changes in effective quantum yield and water potential were accurately predicted using partial least squares regression and the newly developed Water Potential Index 2, respectively. The prediction accuracy of hyperspectral indices and partial least squares regression were similar, as long as a strong relationship between the physiological trait and reflectance was present. This demonstrates that current hyperspectral processing approaches can be used in automated plant phenotyping platforms to monitor physiological traits with a high temporal resolution

3D plant modelling via hyperspectral imaging

Plant phenomics research requires different types of sensors be employed to measure the physical traits of plant surface and to estimate the plant biomass. Of particular interest is the hyperspectral imaging device which captures wavelength indexed band images that characterise material properties of objects under study. In this paper, we introduce a proof of concept research that builds 3D plant model directly from hyperspectral images captured in a controlled lab environment. We show that hyperspectral imaging has shown clear advantages in segmenting plant from its background and is promising in generating comprehensive 3D plant models