EP4256307A1 - Procédé mis en oeuvre par ordinateur, programme informatique, système de traitement de données et dispositif de détermination du comportement de réflectance de la surface d'un objet, et support de stockage contenant des instructions pour la détermination du comportement de réflectance de la surface d'un objet - Google Patents

Procédé mis en oeuvre par ordinateur, programme informatique, système de traitement de données et dispositif de détermination du comportement de réflectance de la surface d'un objet, et support de stockage contenant des instructions pour la détermination du comportement de réflectance de la surface d'un objet

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Publication number
EP4256307A1
EP4256307A1 EP23704170.2A EP23704170A EP4256307A1 EP 4256307 A1 EP4256307 A1 EP 4256307A1 EP 23704170 A EP23704170 A EP 23704170A EP 4256307 A1 EP4256307 A1 EP 4256307A1
Authority
EP
European Patent Office
Prior art keywords
data sets
entries
images
reflectance
recording
Prior art date
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EP23704170.2A
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German (de)
English (en)
Inventor
Tobias NOELL
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Carl Zeiss Vision International GmbH
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Carl Zeiss Vision International GmbH
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Publication of EP4256307A1 publication Critical patent/EP4256307A1/fr
Pending legal-status Critical Current

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/55—Specular reflectivity
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/40—Extraction of image or video features
    • G06V10/60—Extraction of image or video features relating to illumination properties, e.g. using a reflectance or lighting model
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
    • G01N21/27—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands using photo-electric detection ; circuits for computing concentration
    • G01N21/274—Calibration, base line adjustment, drift correction
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/50—Image enhancement or restoration using two or more images, e.g. averaging or subtraction
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/70—Denoising; Smoothing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/77—Retouching; Inpainting; Scratch removal
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/60—Analysis of geometric attributes
    • G06T7/62—Analysis of geometric attributes of area, perimeter, diameter or volume
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/80—Analysis of captured images to determine intrinsic or extrinsic camera parameters, i.e. camera calibration
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/40—Extraction of image or video features
    • G06V10/44—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/74—Image or video pattern matching; Proximity measures in feature spaces
    • G06V10/761—Proximity, similarity or dissimilarity measures
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/47—Scattering, i.e. diffuse reflection
    • G01N21/4785—Standardising light scatter apparatus; Standards therefor
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10141—Special mode during image acquisition
    • G06T2207/10152—Varying illumination
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20212—Image combination
    • G06T2207/20221—Image fusion; Image merging
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30108—Industrial image inspection

Definitions

  • Computer-implemented method Computer program.
  • the present invention relates to a computer-implemented method, a computer program, a data processing system and a device for determining the reflectance behavior of a surface of an object (in particular a spectacle lens, a spectacle frame or spectacles) and a non-volatile computer-readable storage medium with instructions stored thereon for determining the reflectance behavior of a surface of an object (especially a spectacle lens, a spectacle frame or a pair of glasses).
  • the visual appearance of surfaces is largely determined by how incoming light interacts with this surface. Possible interactions are, for example, diffuse scattering (with rough surfaces), directed radiation (with reflective surfaces) or penetration (with transparent surfaces).
  • BRDF bidirectional reflectance distribution function
  • 1145/2461912.2461944 shows a device for detecting the spatially varying reflection properties of 3D objects.
  • the 3D objects are observed under continuous spherical-harmonic lighting conditions.
  • the lighting setup used consists of a rotating arc with controllable LEDs. This creates a full 3D model and a set of composite reflectance maps.
  • US 2014/0268160 A1 shows a device for measuring surface orientation maps of an object.
  • the object is illuminated by a light source with a controllable illumination field, while images of the object are recorded by cameras. From these images, the object's reflection properties are then extracted, including an albedo, a reflection vector, a roughness, and/or anisotropy parameters of a specular reflection lobe.
  • a behavior measured at one point can therefore be transferred to another point on the surface if both points are assigned to the same base material.
  • the base materials are usually assigned based on the color of the surface, ie it is derived from it assumed that two spots of similar color have the same base material.
  • the main difficulty with these methods is recognizing the correct number of base materials and their specific extents and making the appropriate mapping from surface point to base material.
  • Each base material and its associated extent can be referred to as a segment of the surface, so that this approach can also be referred to as a segmentation problem, in which the different segments of the surface are to be determined and as few images as possible are to be recorded for the determined segments for the SVBRDF measurement.
  • the computer-implemented method for determining the reflectance behavior of a surface of an object multiple images of the object are provided, with the images differing in a recording direction and/or in an illumination direction during the recording.
  • Data sets are created with entries that each describe a reflectance value derived from the images and an associated recording direction and an associated illumination direction, each data set being associated with a point on the surface of the object.
  • missing entries for data sets for points on the surface are determined based on entries from data sets already created. Determining the missing entries can include calculating, combining and/or other steps to be performed.
  • a measure of similarity to the reflectance values derived from the images can be taken into account when determining missing entries in the data sets.
  • a mean value calculation or a weighted mean value calculation of the reflectance values derived from the images can be carried out.
  • the reflectance values derived from the images can be smoothed. Thus, from relatively few images, data sets can first be created in which the reflectance values are derived from the images. These are therefore measured reflectance values. The determined missing entries of the data sets are then based on the already created data sets (or their entries) and they are therefore not measured reflectance values, but, for example, calculated entries of the data sets.
  • the complex and error-prone segmentation as in the previously known procedure is not necessary, so that a very good determination of the reflectance behavior of the surface of the object can also be achieved.
  • the reflectance values can be determined for surface points that may not have been recorded or were not illuminated in a specific configuration and are therefore not present on the recordings.
  • the step of adding missing entries in the data records is therefore preferably carried out without a segmentation step.
  • Reflectance is understood here in particular as the degree of reflection and therefore the ratio of the reflected radiant power to the incident radiant power.
  • Reflectance value is understood here in particular as a value that describes the reflectance.
  • Reflectance behavior is understood here in particular as how the light is reflected and/or scattered by the surface of the object. In other words, the reflectance behavior describes the optical impression that a person gains when looking at the surface due to the reflecting, scattering and/or absorbing properties of the surface.
  • Illumination direction is understood here in particular as the angle of incidence (relative to a predetermined coordinate system) at which the surface of the object is illuminated with light during the recording.
  • the recording direction (related to the predetermined coordinate system) is understood here in particular to mean the angle at which the recording of the surface of the object takes place.
  • a measure of similarity to the reflectance values derived from the images can be taken into account.
  • the measure of similarity can take into account at least one selected from the group of similarity of the angular configuration, spatial distance and photometric similarity.
  • the similarity of the angular configuration can in particular involve the deviation in the angle of the recording direction and/or the deviation in the angle of the illumination direction.
  • the spatial distance can in particular be the distance over the surface (geodetic distance).
  • the photometric similarity can in particular be the similarity of the reflectance values derived from the images. The smaller the respective difference, the more likely the similarity is assumed.
  • a mean value calculation or a weighted mean value calculation of the reflectance values derived from the images can be carried out.
  • the calculation of the mean can in particular be the calculation of the arithmetic mean.
  • Another parameter can be taken into account in the weighted mean calculation, such as the distance to the point on the surface for which the further data set is to be calculated. The greater the distance, the smaller the influence of an already known reflectance value s.
  • the reflectance values derived from the images can be smoothed. This means in particular that the measured values are averaged in order to be able to better correct undesired outliers.
  • the derived reflectance values are subjected to low-pass filtering for smoothing.
  • the step of providing the multiple images of the object can include the step of illuminating and taking the picture.
  • the images can have a plurality of pixels, which are then assigned to corresponding image points and a combination of recording direction and illumination direction.
  • the data sets can be parameterized in such a way that a four-dimensional angle component with two parameters for the illumination direction and two parameters for the recording direction and a two-dimensional spatial component with two parameters for the corresponding point on the surface of the object is present.
  • a discretization of this function is achieved by the data sets.
  • a continuously spatially varying bidirectional reflectance distribution function can be calculated based on the data sets. This means in particular that the function is continuous.
  • photo-realistic representations of the object can be created in this way.
  • the reflectance behavior of the surface of the object can be determined using the data sets.
  • the object can in particular be a spectacle lens, a spectacle frame or spectacles.
  • step a) at least one of the sequences of steps a) or b) can be carried out for the calibration.
  • step a) at least a first calibration image of a calibration object with a plurality of distinguishable markers at positions that are known relative to one another is evaluated, at least a second calibration image of the calibration object, which was illuminated with a pattern when the image was recorded, is evaluated and the calibration is then based on carried out the evaluations of the first and second calibration image(s).
  • step b) at least a first calibration image of a calibration object is evaluated with a plurality of distinguishable spheres at positions that are known relative to one another, with the center points of the spheres being determined, and the calibration is based on the evaluation(s) of the first calibration image(s). ) carried out.
  • a calibration an improved determination of the reflectance behavior of the surface of the object is made possible.
  • At least a fourth calibration recording of a calibration object can be evaluated in the computer-implemented method for a calibration.
  • This calibration object has at least one non-repetitive pattern, whereby characteristic features of the surface of the calibration object are specified, a variable size, several calibration bodies with different sizes, a color chart for color calibration of cameras for generating the recordings, or characteristic features with known scaling and/or or known relative distances to each other.
  • a calibration object can be recorded with at least one camera of the at least one camera-projector combination(s).
  • a step IV) one or more patterns on the calibration object is projected with at least one of the projectors camera-projector combination (s), which of the at least one camera of the camera-projector combination(s) is recorded.
  • a step V) based on the recordings of the at least one camera of the camera-projector combination(s), a calibration of the at least one camera of the camera-projector combination(s) and/or of the at least one projector of the camera-projector Combination(s) carried out.
  • the relative positioning of the calibration object for at least one camera of the camera-projector combination be changed after performing step sequences III) and IV) and then perform step sequences III) and IV) again.
  • a camera response curve of the at least one camera of the camera-projector combination(s) can be calibrated based on recordings with different exposure times.
  • a bidirectional reflectance distribution function can be used to create the data sets, which describes the amount of reflected light of an exiting beam for each possible observation direction as a BRDL value for each point on the surface of the object for possible light incidence angles of an incident beam, see above that the reflectance distribution function is a six-dimensional puncture, which has a four-dimensional angular component with two parameters for the light incidence direction and two parameters for the observation direction, and a two-dimensional spatial component with two parameters for the corresponding point on the surface of the object.
  • This parameterization enables a good description of the reflectance behavior of the object's surface.
  • a bidirectional reflectance distribution function can also be used, which describes the amount of reflected light of an emerging beam in each possible observation direction as a BRDF value for each point on the surface of the object for possible light incidence angles of an incident beam, so that the reflectance distribution function (SVBRDF) is a six-dimensional function that has a four-dimensional angle part with two parameters for the direction of light incidence and two parameters for the direction of observation as well as a two-dimensional spatial part with two parameters for the corresponding point on the object surface, the four-dimensional angle part of the reflectance distribution function using a Rusinkiewicz Parameterization can be parameterized in such a way that the transformed coordinate axes are polar angles and azimuth angles of a mean vector and polar angles and azimuth angles of a differential vector, with the mean vector being the mean vector between the incident and emerging beam and the differential vector being the difference between the incident beam and the mean vector.
  • BRDF bidirectional reflectance distribution function
  • the space spanned by the transformed coordinate axes is then discretized by means of a Coordinate lattice structure carried out with lattice points, the lattice spacing for the coordinate axis polar angle of the central vector being smaller than for the remaining transformed coordinate axes.
  • a reflectance measurement is carried out for each grid point of the surface of the object to measure the BRDF value.
  • a BRDF table is constructed for each grid point using the measured BRDF values from the data sets, and the BRDF tables for unmeasured surface points are completed by calculating the BRDF values of the unmeasured surface points from the measured BRDF values , in order to describe the reflectance behavior of the surface of the object.
  • a computer program for determining the reflectance behavior of a surface of an object based on a plurality of provided images of the object, which differ in a recording direction and/or illumination direction of the recording, the computer program comprising instructions which, when executed on a computer cause the computer to create data sets with entries each describing a reflectance value derived from the images and an associated recording direction and an associated illumination direction, each data set being associated with a point on the surface of the object. Furthermore, the instructions cause the computer to determine missing entries in the data sets for points on the surface of the object based on entries in data sets that have already been created. Determining the missing entries can include calculating, combining and/or other steps to be performed.
  • a measure of similarity to the reflectance values derived from the images can be taken into account when determining missing entries in the data sets.
  • a mean value calculation or a weighted mean value calculation of the reflectance values derived from the images can be carried out.
  • the reflectance values derived from the images can be smoothed.
  • a very good determination of the reflectance behavior of a surface of the object can thus be achieved in an advantageous manner with only a few images.
  • a non-transitory computer-readable storage medium with instructions stored thereon for determining the reflectance behavior of a surface of an object based on a plurality of provided images of the object, which differ in a recording direction and/or illumination direction during recording, provided, the instructions, if they are executed on a computer, cause the computer to create data sets with entries that each describe a reflectance value derived from the images and an associated recording direction and an associated illumination direction, each data set being associated with a point on the surface of the object.
  • the instructions cause the computer to determine missing entries in the data sets for points on the surface of the object based on entries in data sets that have already been created. Determining the missing entries can include calculating, combining and/or other steps to be performed. According to the invention, a measure of similarity to the reflectance values derived from the images can be taken into account when determining missing entries in the data sets. Alternatively or additionally, when missing entries in the data sets are determined, a mean value calculation or a weighted mean value calculation of the reflectance values derived from the images can be carried out. As a further alternative or in addition, when missing entries in the data sets are determined, the reflectance values derived from the images can be smoothed.
  • a very good determination of the reflectance behavior of the surface of the object can thus advantageously be achieved with just a few images.
  • a data processing system for determining the reflectance behavior of a surface of an object based on a plurality of provided images of the object, which differ in a recording direction and/or illumination direction during recording, the data processing system comprising a processor and a memory.
  • the processor is designed to create data sets with entries based on instructions from a computer program stored in the memory, each of which describes a reflectance value derived from the images and an associated recording direction and an associated illumination direction, with each data set being associated with a point on the surface of the object .
  • the processor is designed, based on the instructions of the computer program stored in the memory, to determine missing entries in the data sets for points on the surface of the object based on entries in data sets that have already been created.
  • Determining the missing entries can include calculating, combining and/or other steps to be performed.
  • a measure of similarity to the reflectance values derived from the images can be taken into account when determining missing entries in the data sets.
  • a mean value calculation or a weighted averaging of the reflectance values derived from the images can be performed.
  • the reflectance values derived from the images can be smoothed.
  • a very good determination of the reflectance behavior of the surface of the object can thus advantageously be achieved with just a few images.
  • a device for determining the reflectance behavior of a surface of an object comprising a control unit and a detection unit, the detection unit being set up to record a plurality of images of the object which are in a recording direction and/or an illumination direction during the recording distinguish, and wherein the control unit is set up to create data sets with entries that each describe a reflectance value derived from the images and an associated recording direction and an associated illumination direction, each data set being associated with a point on the surface of the object, and missing entries of Records for points of the object determined based on entries already created datasets.
  • the reflectance behavior of the surface of an object can be determined using the data sets created in this way.
  • control unit is set up to take into account a measure of similarity to the reflectance values derived from the images.
  • control unit is also set up to carry out a mean value calculation or a weighted mean value calculation of the reflectance values derived from the images.
  • control unit is also set up to smooth the reflectance values derived from the images.
  • a method for determining the reflectance behavior of a surface of an object which has the following steps:
  • BRDF bidirectional reflectance distribution function
  • SVBRDF reflectance distribution function
  • the data records can be combined into the BRDF tables.
  • At least one selected from the group of similarity of the angular configuration, spatial distance (e.g. over the surface) and photometric similarity can be taken into account as a measure of similarity.
  • the similarity of the angular configuration in particular, the similarity increases as the distance decreases; distance is, and for photometric similarity in particular, similarity increases with decreasing distance.
  • An averaging calculation or a weighted averaging calculation can be performed in calculating the BRDF values of the unmeasured point of the surface from the measured BRDF values.
  • the measured BRDF values can be smoothed when creating the BRDF tables for each grid point with the measured BRDF values.
  • a continuous bidirectional reflectance distribution function can be calculated.
  • a multivariate interpolation that is, an interpolation for a function with several variables
  • a parameter estimation of the parameterized reflectance distribution function can be performed.
  • the device according to the invention can be further developed in the same way as the method according to the invention (this also applies vice versa).
  • 1 shows a schematic view of an embodiment of the device according to the invention
  • 2 shows a schematic representation to explain the spherical coordinates for the incident ray 9 and the emerging ray 12
  • FIG. 3 shows a representation according to FIG. 2 to explain the Rusinkiewicz parameterization that was carried out.
  • a 3D digitizing system 1 shown in FIG. 1 (which can also be referred to as a measuring system 1 or as a device 1 for determining the reflectance behavior of a surface of an object), it comprises a control unit 2, an illumination unit 3 that can be positioned in space, a detection unit 4 that can be positioned in space and a holder 5 on which an object 6 to be digitized is positioned.
  • the holder 5 can be designed, for example, as a rotatable glass plate.
  • the 3D digitizing system 1 is designed in particular in such a way that it can be used to determine a description of the reflectance behavior of the surface 7 of the object 6 .
  • the lighting unit 3 has a light source 8, which can direct a light beam 9 (incident beam) onto the surface 7 of the object 6, and a first movement unit 10, which changes the position of the light source 8 in space (and thus the direction of the light beam 9 and the point of impact of the light beam 9 on the surface 7) can adjust and change.
  • the detection unit 4 includes a detector 11 for detecting the light beam 12 reflected by the surface 7 and a second movement unit 13 with which the position of the detector 11 in space can be set and changed.
  • the holder 5 is rotatable, as indicated by the arrow 14. The surface 7 of the object 6 can thus be illuminated at different points with different angles and the reflection or scattering can be detected at different angles.
  • the control unit 2 can carry out all the calculation and evaluation steps described below. However, it is also possible that the control unit 2 only controls the measurement steps described below and transmits the data obtained thereby to a separate calculation unit 15, as indicated by dashed lines in FIG. 1, and the calculation unit 15 carries out the calculation and evaluation steps.
  • the calculation unit 15 can be a computer with a processor, the same applies to the control unit 2.
  • BRDF bidirectional reflectance distribution function
  • the BRDF has two additional parameters for identifying the respective location on the surface 7, so that the bidirectional Reflectance distribution function is a six-dimensional function, which is a four-dimensional angular component with two parameters for the direction of light incidence and two parameters for the direction of observation, and a two-dimensional spatial component with two parameters for the corresponding points on the object surface.
  • the bidirectional reflectance distribution function can also be referred to as a spatially varying bidirectional reflectance distribution function (e.g. specially varying BRDF or SVBDF).
  • the four-dimensional angle component is parameterized in the six-dimensional bidirectional reflectance distribution function using the Rusinkiewicz parameterization in such a way that the transformed coordinate axes are polar angles 0h and Azemuth angle h of a mean vector h and polar angles 0d and azemuth angle ⁇ t> (i a difference vector d exist.
  • the mean vector h is the mean vector between incident ray 9 and outgoing ray 12 and the difference vector d is the difference of incident ray 9 and mean vector h (the mean vector h as well as the difference vector d are each given here in bold type and can also be given as h and d.)
  • the Rusinkiewicz parameterization is described in the article "A New Change of Variables for Efficient BRDF Representation" Szymon M. Rusinkiewicz, Stanford University described in detail, which can be downloaded from https://www.cs.princeton.edu/ ⁇ smr/papers/brdf_change_of_variables/, for example, and was presented in 1998 at the Eurographics Workshop on Rendering.
  • This parameterization is based on a reflection, shown schematically in Fig. 2, of the incident light beam 9 (which runs in the opposite direction to the arrow shown) at a point on the surface 7, which leads to a reflected light beam 12 and is shown in Fig. 2 in spherical coordinates, in Connection with Fig. 3 clarifies, since in Fig. 3 the same reflection is shown in the Rusinkiewicz parameterization.
  • the incident light beam 9 is clearly described with the spherical coordinates 0i and ⁇ t>, based on the surface normal n and the surface tangent t.
  • the reflected light beam 12 has the angles 0 0 and ⁇ t> 0 .
  • this reflection is described by the transformed coordinate axes 0h, h, 0 d , O d . Further details result from the following description of the vectors.
  • 0 ⁇ and 0 ⁇ are the spherical coordinates of the mean vector h in the t-n-b system and the two rotations for d bring the mean vector h to the north pole and are the spherical coordinates 0[ and O; of the incident ray 9 in this transformed system (further details can be found in reference [1] and in the Rusinkiewicz article “A New Change of Variables for Efficient BRDF Representation”).
  • the BRDF values of many materials occurring in practice show a strong change in the 0h direction and a smaller change along the other coordinate axes 0 d , h and O d . Therefore, the space spanned by the Rusinkiewicz parameterization is discretized by means of a lattice structure, with a smaller lattice spacing being chosen along the 0h direction than for the directions of the other coordinate axes, since greater changes in the reflectance behavior are to be expected along the 0h direction.
  • the result is an "angle table" that contains a number of angle configurations from the direction of light incidence and the direction of observation that are important for the reflectance behavior.
  • the associated BRDF values of the corresponding surface points are now sought for each of the nine entries in the angle table and are assigned to these entries.
  • This combination of discrete angles (corresponding to the entries from the angle table) with the associated BRDF values is also referred to below as the BRDF table.
  • a BRDF table is given schematically for the index value 2 according to the angle table, whereby to simplify the description, 25 points of the surface 7 with the coordinates K1 and K2 are assumed and measured values are only available if none entered in the corresponding "Value" column.
  • the unmeasured BRDF values are determined and the BRDF tables are thus completed.
  • this problem can be treated as a "scattered data interpolation" problem.
  • a basic idea is that unknown (unmeasured) BRDF values per surface point can be calculated using other known (measured) BRDF values can be determined from the remaining BRDF tables if it can be assumed that the surface 7 reacts in a similar way.
  • Unknown BRDF values are thus determined using known BRDF values (e.g. by a weighted average or other interpolation techniques) which have a high degree of similarity with respect to the previous three considerations. It is also possible that measured BRDF values can be corrected in a similar way, e.g. to perform smoothing. In this way the unknown BRDF values are filled in and thus the entire SVBRDF is determined without having to determine a number of base materials.
  • Each row of a BRDF table can also be used as a data record DS with at least one entry (e.g.
  • Illumination direction are referred to, each data set being assigned to a point on the surface 7 of the object 6.
  • a continuous SVBRDF can then be determined based on the felled BRDF tables. This is possible, for example, by multivariate interpolation or parameter estimation of a parameterized BRDF model.

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Abstract

L'invention concerne un procédé mis en œuvre par ordinateur pour déterminer le comportement de la réflectance de la surface (7) d'un objet (6), comportant les étapes suivantes : la fourniture de plusieurs images de l'objet (6), lesdites images différant quant à la direction de capture et/ou la direction d'éclairage au cours du processus de capture, la génération d'ensembles de données avec des entrées, chacune décrivant une valeur de réflectance dérivée des images, une direction de capture assignée et une direction d'éclairage assignée, chaque ensemble de données étant assigné à un point respectif de la surface de l'objet (6), et la détermination des entrées manquantes des ensembles de données sur la base des entrées des ensembles de données déjà générés, un degré de similitude avec les valeurs de réflectance dérivées des images étant pris en considération, une valeur moyenne ou une valeur moyenne pondérée des valeurs de réflectance dérivées des images étant calculée, ou les valeurs de référence dérivées des images étant soumises à un processus de lissage.
EP23704170.2A 2022-02-23 2023-02-14 Procédé mis en oeuvre par ordinateur, programme informatique, système de traitement de données et dispositif de détermination du comportement de réflectance de la surface d'un objet, et support de stockage contenant des instructions pour la détermination du comportement de réflectance de la surface d'un objet Pending EP4256307A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
EP22158278.6A EP4235149A1 (fr) 2022-02-23 2022-02-23 Procédé mis en oeuvre par ordinateur, programme informatique, système de traitement de données et dispositif de détermination du comportement de réflectance d'une surface d'un objet, ainsi que support d'enregistrement non volatil lisible par ordinateur pourvu d'instructions mémorisées sur celui-ci destinées à la détermination du comportement de réflectance d'une surface d'un objet
PCT/EP2023/053625 WO2023161083A1 (fr) 2022-02-23 2023-02-14 Procédé mis en oeuvre par ordinateur, programme informatique, système de traitement de données et dispositif de détermination du comportement de réflectance de la surface d'un objet, et support de stockage contenant des instructions pour la détermination du comportement de réflectance de la surface d'un objet

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EP4256307A1 true EP4256307A1 (fr) 2023-10-11

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EP22158278.6A Pending EP4235149A1 (fr) 2022-02-23 2022-02-23 Procédé mis en oeuvre par ordinateur, programme informatique, système de traitement de données et dispositif de détermination du comportement de réflectance d'une surface d'un objet, ainsi que support d'enregistrement non volatil lisible par ordinateur pourvu d'instructions mémorisées sur celui-ci destinées à la détermination du comportement de réflectance d'une surface d'un objet
EP23704170.2A Pending EP4256307A1 (fr) 2022-02-23 2023-02-14 Procédé mis en oeuvre par ordinateur, programme informatique, système de traitement de données et dispositif de détermination du comportement de réflectance de la surface d'un objet, et support de stockage contenant des instructions pour la détermination du comportement de réflectance de la surface d'un objet

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EP22158278.6A Pending EP4235149A1 (fr) 2022-02-23 2022-02-23 Procédé mis en oeuvre par ordinateur, programme informatique, système de traitement de données et dispositif de détermination du comportement de réflectance d'une surface d'un objet, ainsi que support d'enregistrement non volatil lisible par ordinateur pourvu d'instructions mémorisées sur celui-ci destinées à la détermination du comportement de réflectance d'une surface d'un objet

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US (1) US11928851B2 (fr)
EP (2) EP4235149A1 (fr)
CN (1) CN116940827A (fr)
WO (1) WO2023161083A1 (fr)

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6765573B2 (en) * 2000-10-26 2004-07-20 Square Enix Co., Ltd. Surface shading using stored texture map based on bidirectional reflectance distribution function
EP1880196B1 (fr) * 2005-04-25 2019-10-16 X-Rite, Inc. Mesure d'une propriete d'aspect d'une surface au moyen d'une fonction de distribution de reflexion bidirectionnelle spatialement sous-echantillonnee
DE102008003906B4 (de) * 2008-01-10 2009-11-26 Rodenstock Gmbh Verwendung eines Fixationstargets und Vorrichtung
DE102008046988A1 (de) * 2008-09-12 2010-04-22 [0X1] Software Und Consulting Gmbh Reflektometer und Verfahren zur Charakterisierung von Materialien und Materialoberflächen zumindest hinsichtlich optischer Streueigenschaften oder/und optischer Reflektionseigenschaften
CN102117488B (zh) * 2011-02-21 2014-06-25 北京航空航天大学 一种虚拟环境中构造真实物体表面反射特性的方法
FR2987920B1 (fr) * 2012-03-08 2018-03-02 Essilor International Procede de determination d'une caracteristique geometrico-morphologique, de posture ou comportementale d'un porteur d'une paire de lunettes
US9357204B2 (en) * 2012-03-19 2016-05-31 Fittingbox Method for constructing images of a pair of glasses
WO2014153022A1 (fr) * 2013-03-14 2014-09-25 University Of Southern California Scanneur d'objet spéculaire pour mesurer des propriétés de facteur de réflexion d'objets
CN103955958B (zh) * 2014-04-11 2016-08-17 浙江大学 一种基于Kinect设备的交互式材质外观采集方法
US10311163B2 (en) * 2014-06-30 2019-06-04 Microsoft Technology Licensing, Llc Non-parametric microfacet factor models for isotropic bidirectional reflectance distribution functions
DE102015209487A1 (de) * 2015-05-22 2015-07-23 Carl Zeiss Smt Gmbh Verfahren zum Ermitteln eines korrigierten Intensitätsverhältnisses
JP6894672B2 (ja) * 2016-05-18 2021-06-30 キヤノン株式会社 情報処理装置、情報処理方法、プログラム
FR3087891B1 (fr) * 2018-10-24 2020-11-20 Entpe Dispositif optique pour la mesure des proprietes optiques des materiaux.
CN112634156B (zh) * 2020-12-22 2022-06-24 浙江大学 基于便携式设备采集图像估计材质反射参数的方法

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US11928851B2 (en) 2024-03-12
CN116940827A (zh) 2023-10-24
EP4235149A1 (fr) 2023-08-30
US20230386172A1 (en) 2023-11-30
WO2023161083A1 (fr) 2023-08-31

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