Reflectance Transformation Imaging Method for Large-Scale Objects

Abstract

RTI is an image-based rendering method which can represent the appearance of an object under varying illuminations. To create realistic synthetic-images using RTI, it is necessary to take dozens of images on a mounted camera with a calibrated point light source. Conventional RTI methods have proposed complex lighting systems in a hemispherical dome, or manually calibrate light poses using a reflective probe. In most cases, those methods are not suitable for the large-scale object in an outdoor environment because the size of the target object is restricted by the configuration of measurement systems. In this paper, we present a new RTI method which can create photorealistic images of a large scale outdoor scene under arbitrary light directions. Instead of capturing RTI samples at a time for an entire domain, we divide the RTI domain into a set of subsections. RTI samples in each section are acquired using a camera and an uncalibrated light source. After acquiring samples, we estimate svBRDF of measured samples without any prior knowledge of a 3D model, light poses, and surface normals. We also present an approach to merge the partial RTI images into a panoramic image. Experimental results show that our framework can extend the RTI methods applicable to large-scale objects.

Flatbed scanners as versatile tools for studying surface details of compression fossils

“…While it is possible and, in some situations, even necessary to record a Multi Light Image Collection by manually repositioning a light source between image captures, this makes the image acquisition tedious and requires including reference spheres in all images to estimate the illumination directions (Cosentino, 2013;Kim et al, 2016;Porter et al, 2016). Ring or dome-shaped arrays of individually controllable lights allow for a much more rapid image acquisition and spare the need to include reference spheres during the actual documentation, as the illumination configuration can be kept constant, and the light directions can be 6 estimated beforehand or afterwards (Hammer et al, 2002;Brecko and Mathys, 2020;Hughes-Hallett et al, 2021;Tao et al, 2021).…”

“…Nevertheless, CCDtype flatbed scanners can provide a convenient and accessible alternative to macro photographic setups with dedicated lighting gear (expensive if not custom-built). Unlike when manually repositioning a light source (e.g., Kim et al, 2016), when using CCD-type scanners, one is not required to have reference spheres in the field of view of every recorded image. This is because the inclination angle of the scanner illumination is constant and when adhering to a standardized rotation routine (e.g., four images with 90-degree rotation in between), one can reuse previously estimated light directions (stored as light point files).…”

The earliest beetle †Coleopsis archaica (Insecta: Coleo­ptera) – morphological re-evaluation using Reflectance Transformation Imaging (RTI) and phylogenetic assessment

“…While it is possible to produce RTI models of very small objects, the constraints of macro-photography regarding the depth of field and the field of view limit the application of a conventional RTI procedure (Cosentino 2013;Hughes-Hallett et al 2021). Specialised workflows for overcoming the limited field of view through panoramic stitching (Kim et al 2016;Aure et al 2017), and for solving the problem of the narrow depth of field through focus merging (Lewis et al 2021) have already been presented. However, they have not been applied to fossils of insects so far.…”

“…Naturally, these two challenging factors also apply to images taken with an RTI setup. Solutions to overcome the limitations of a narrow field of view by creating panoramic images (Kim et al 2016;Aure et al 2017), and the limitations regarding the depth of field by focus-merging (Lewis et al 2021) have already been demonstrated separately. However, to our knowledge have never been applied to RTI data of fossils.…”

Reflectance Transformation Imaging Visual Saliency: Local and Global Approaches for Visual Inspection of Engineered Surfaces

“…Therefore, depending on the density of the acquisition angles and the methods chosen, RTI acquisition can lead to obtaining a voluminous and complex set of data [18,32,33]. Estimation of the descriptors constitutes a first step in data reduction, which allows a better understanding of the local surface characteristics and facilitates the analysis and postprocessing carried out based on this type of acquisition.…”