Color Acquisition, Management, Rendering, and Assessment in 3D Reality-Based Models Construction

Color Acquisition, Management, Rendering, and Assessment in 3D Reality-Based Models Construction

Marco Gaiani (Università di Bologna, Italy)
DOI: 10.4018/978-1-4666-8379-2.ch001
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In this chapter are presented a framework and some solutions for color acquisition, management, rendering and assessment in Architectural Heritage (AH) 3D models construction from reality-based data. The aim is to illustrate easy, low-cost and rapid procedures that produce high visual accuracy of the image/model while being accessible to non-specialized users and unskilled operators, typically Heritage architects. The presented processing is developed in order to render reflectance properties with perceptual fidelity on many type of display and presents two main features: is based on an accurate color management system from acquisition to visualization and more accurate reflectance modeling; the color pipeline could be used inside well established 3D acquisition pipeline from laser scanner and/or photogrammetry. Besides it could be completely integrated in a Structure From Motion pipeline allowing simultaneous processing of color/shape data.
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In the field of Architectural Heritage (AH) analysis, conservation and management, color definition and reproduction is a key step, as demonstrated by the outstanding Henri Labrouste and Louis Duc drawings representing roman monuments, made in the years 1825-1830 (Gaiani, 2012) and as shown by today many attempts to faithfully reproduce the color in urban planning documentation (Falzone, 2008).

To have a faithful color characterization and reproduction, in the restoration field, many techniques have been developed and today it is possible to refer to three approaches (Santopuoli & Seccia, 2008):

  • Sample transcription: this technique requires a suitable support on which reproduce the color. The results are highly dependent on the expertise and the visual observation ability of the examiner. The method is useful for cataloguing but, being completely subjective, it cannot be used for reproduction;

  • Visual comparison with color atlas (i.e. the ‘Munsell book of color’): also this technique is highly dependent on the expertise and the visual observation ability of the surveyor;

  • Diffuse reflectance measurement with instruments such as colorimeters (given the chromaticity CIE L*a*b* coordinates of the color), spectrophotometers (which, in addition, provide the curve of the diffuse reflectance as a wavelength function) or telephotometers (which output the same data of spectrophotometers, but with the ability to operate at relevant distances from the sample). These are the most accurate, simple and flexible tools available today. They provide excellent control of reflectance and color, but, covering a small area, in case of non-uniform color (almost always), they still require countless readings.

These techniques, beyond the ability of the operator, are unable to ensure a right color checking on a wide surface, and with a non-uniform color, that is the typical condition of AH (Apollonio et al., 2011).

Furthermore, they aren’t able neither to exploit extensively digital media and digital techniques, nor to ensure the correct perception of a AH artifact color on a RGB display (i.e. LCD displays or DLP video projectors), nor to ensure its faithful reproduction on a printed support.

Conversely, the color characterization and reproduction using digital images is a powerful solution.

The problem of an accurate color description and reproduction using images could today be depicted as the problem to faithfully determine the color and tone level and can be solved by the chromatic and tonal definition (Reinhard et al., 2008).

The fidelity of color reproduction depends on a number of variables such as the lighting level during the acquisition step, the technical characteristics of the acquisition system, and the mathematical representation of color information throughout the acquisition and reproduction pipeline.

In particular, the values of a color in an image are the result of the interaction of the incident illumination, the object geometry, the object reflectance and the camera transfer function.

In general, the solution of this problem requires understanding and controlling environmental and artificial light sources over the measurement set. When illumination is reliably known, parameters for a surface reflectance function can be estimated using the image values (Lensch et al., 2003).

AH artifacts implies outdoor environments, where natural light characteristics are extremely complex and changeable; scenes are characterized by many elements belonging to different planes, curved surfaces reacting to light in several ways; we match with a wide range of materials characterized by different values of light reflection, porosity, transparency, etc. Therefore we cannot design a basis set of lighting conditions. For this reason the color capture and reproduction of masonry faces, historical architectural handmade and monumental-historical buildings, is a very complex issue.

These difficulties increase when the problem of chromatic and tonal definition and reproduction is addressed in the context of 3D reality-based models construction and visualization (Allen et al., 2004).

Key Terms in this Chapter

Interest Points Detection and Description for Structure From Motion: Interest Point in an image is a point that is exceptional from its neighborhood. To detect and describe this point typically it’s used a two-step process: A. Feature Detectors: where a feature detector (extractor) is an algorithm taking an image as input and outputting a set of regions (‘local features’). B. Feature Descriptor: where a descriptor is computed on an image region defined by a detector. The descriptor is a representation of the intensity (e.g. color) function on the region. Main requirement of feature detectors and descriptors algorithms is that good features should be robust to all sorts of changes that can occur between images: illumination, scale, rotation, affine transformation, full perspective.

Retroreflection: the reflection that occurs when surfaces return a portion of the directed light to its source. This is why retroreflective materials appear brightest to observers located near the light source – a driver and the vehicle headlights, for example. This is true for drivers at almost any viewing angle, which makes retroreflective surfaces excellent for night visibility.

Diffuse Reflection: The reflection of light from a surface such that an incident ray is reflected at many angles rather than at just one angle as in the case of specular reflection. An illuminated ideal diffuse reflecting surface will have equal luminance from all directions which lie in the half-space adjacent to the surface (Lambertian reflectance).A surface built from a non-absorbing powder such as plaster, or from fibers such as paper, or from a polycrystalline material such as white marble, reflects light diffusely with great efficiency.

Color Profile: A color profile is a numerical model of a color space. Operating systems and programs need to have access to a profile that describes the meaning of the color values in order to interpret the color correctly. Proper color management requires all image files to have an embedded profile. Profiles describe the color attributes of a particular device or viewing requirement by defining a mapping between the device source or target color space and a profile connection space (PCS). This PCS is either CIELAB (L*a*b*) or CIEXYZ. Mappings may be specified using tables, to which interpolation is applied, or through a series of parameters for transformations. ICC ( International Color Consortium ) defined the most common profile format ICC. ICC 4.0 format is today the standard for device profiles.

Bidirectional Reflectance Distribution Function (BRDF): A four-dimensional function that defines how light is reflected at an opaque surface and is a function of illumination geometry and viewing geometry. The function takes a negative incoming light direction, and outgoing direction, both defined with respect to the surface normal, and returns the ratio of reflected radiance exiting along outgoing direction to the irradiance incident on the surface from incoming light direction. Each direction is itself parameterized by azimuth angle and zenith angle; therefore the BRDF is a 4-dimensional function. The BRDF depends on wavelength and is determined by the structural and optical properties of the surface, such as shadow-casting, multiple scattering, mutual shadowing, transmission, reflection, absorption and emission by surface elements, facet orientation distribution and facet density. Specifically, the BRDF is an approximation of the BSSRDF, bi-directional sub-surface scattering reflectance distribution function. The BRDF ignores sub-surface scattering and assumes that the light striking the surface at some point will be reflected from that same point. The main characteristics of a physically plausible BRDF are the symmetry between incident and reflected directions (Helmholtz reciprocity) and that the total reflected power for a given direction of incident radiation is less than or equal to the energy of the incident light (Energy conservation). Some, but not all, BRDFs have a property called isotropy: they are unchanged if the incoming and outgoing vectors are rotated by the same amount about the surface normal. With isotropy, a useful simplification may be made: the BRDF is really a three-dimensional function in this case, and depends only on the difference between the azimuthal angles of incidence and exitance.

CIELAB (or CIE L*a*b*, CIE Lab): Color space in which values L*, a* and b* are plotted using Cartesian coordinate system. Equal distances in the space approximately represent equal color differences. Value L* represents lightness; value a* represents the red/green axis; and value, b* represents the yellow/blue axis. CIELAB is a popular color space for use in measuring reflective and transmissive objects.

Specular Reflection: The mirror-like reflection of light (or of other kinds of wave) from a surface, in which light from a single incoming direction is reflected into a single outgoing direction. This behavior is described by the law of reflection, which states that the direction of incoming light (the incident ray), and the direction of outgoing light reflected (the reflected ray) make the same angle with respect to the surface normal, thus the angle of incidence equals the angle of reflection, and that the incident, normal, and reflected directions are coplanar.

Reflectance (Reflectivity): The ratio of the flux reflected to that incident on a surface. That varies according to the wavelength distribution of the incident radiation. In general it must be treated as a directional property that is a function of the reflected direction, the incident direction, and the incident wavelength. According to the CIE, reflectivity is distinguished from reflectance by the fact that reflectivity is a value that applies to thick reflecting objects. When reflection occurs from thin layers of material, internal reflection effects can cause the reflectance to vary with surface thickness. Reflectivity and reflectance generally refer to an electromagnetic power, while the term ‘reflection coefficient’ is used for the ratio of electric field reflected. The reflectance or reflectivity is thus the square of the magnitude of the reflection coefficient. The reflection coefficient can be expressed as a complex number as determined by the Fresnel equations for a single layer, whereas the reflectance (or reflectivity) is always a positive real number. Many common materials exhibit a mixture of specular and diffuse reflection.

Color Management: In digital imaging systems, color management is a process where the color characteristics for every device in the imaging chain is known precisely and utilized in color reproduction. The primary goal of color management is to obtain a good match across color devices; for example, the colors of one frame of a video should appear the same on a computer LCD monitor, on a plasma TV screen, and as a printed poster. Color management helps to achieve the same appearance on all of these devices, provided the devices are capable of delivering the needed color intensities. In digital photography, this imaging chain usually starts with the camera and concludes with the final print, and may include a display device. Color management cannot guarantee identical color reproduction, as this is rarely possible, but it can at least give you more control over any changes that may occur. The color management system was standardized by the International Color Consortium (ICC) ( AU122: The URL has been redirected to Please verify the URL. ), and is now used in most computers.

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