FR2950139A1 - Method for constructing three-dimensional model to scan e.g. casing of statue, involves projecting structured luminous pattern on surface, acquiring stereoscopic images of surface, and constructing scanning model from images - Google Patents

Abstract

The method involves projecting a structured luminous pattern on a surface with respect to orientation of a scanner. Stereoscopic images of the surface are acquired by acquiring images quadruplet, where the images quadruplet comprises a pair of structured bi-dimensional stereoscopic images of the surface and a pair of textured bi-dimensional stereoscopic images. A scanning model is constructed from the images. Independent claims are also included for the following: (1) a computer program product comprising instructions for executing a method for constructing a three-dimensional model of a physical surface (2) a three-dimensional scanning system comprising a CPU.

Description

Three-dimensional scanning method comprising acquiring a quadruplet of stereoscopic imaqes

The invention relates to the construction of three-dimensional numerical models of physical surfaces by optical measurement, without contact. This technique is commonly called three-dimensional scanning, 3D scanning, or, in English terminology, 3D scanning. The architecture of vision systems for 3D scanning a surface conventionally comprises two components: - a physical component: the scanner (preferably portable), which performs an image acquisition of the surface, usually by a succession shots (point, continuous or continuous) of it; a software component for processing the images produced by the scanner, this processing making it possible to carry out a three-dimensional reconstruction of surface synthesis, for example in a computer-assisted design (CAD) or computer-aided design (CAD / CAM) environment. The acquisition of the images is generally carried out by projecting on the surface to be digitized a structured luminous pattern having a predetermined shape (lines, squares, speckles, etc.), then by optical capture of the projected pattern. Treatments are applied to the captured images, making it possible to calculate the spatial coordinates of a selection of points to reconstruct the three-dimensional model of the surface. On the basis of this same fundamental principle, many solutions have been proposed, which vary according to three main axes: scanner architecture; the nature and form of the projected motif; the reconstruction methodology. In the first place, scanners are generally divided into two types: single-camera scanners (monocular vision), and multi-camera scanners (binocular vision or stereovision, multi-ocular vision). In scanners operating in monocular vision, only one image of the deformed pattern is produced. This image is then compared N003 B004 FR_recalage_texture_TQD to a reference image of the undistorted pattern to perform the reconstruction. US Patent 5,003,166 (MIT), FR 2,842,591 (ENSMA / CNRS), WO2007 / 043036 (PRIME SENSE), US 2009/0059241 (ARTEC) and FR 2,921,719 (NOOMEO) illustrate this architecture. In scanners operating in binocular or multiocular vision, which are the ones we are interested in here, at least two images of the deformed pattern are produced simultaneously, and a comparison of these images is made to perform the reconstruction. International application WO 2006/094409 (CREAFORM) illustrates this architecture. As regards secondly the projected pattern, it may be repetitive, for example in the form of lines or segments (see US 5,003,166 cited above), or non-repetitive, for example in the form of a mouchetis (cf. FR 2 842 591 and FR 2 921 719 cited above). Lastly, as regards the reconstruction methodology, it can vary from one solution to another in the choice of algorithms, but generally comprises three steps: image rectification; - matching; the calculation of the spatial coordinates of points of the digitized surface. In stereovision, the rectification of images is preferable to facilitate their subsequent exploitation. In addition to correcting the distortions induced by the optical systems, images are generally applied to an epipolar rectification treatment to make the epipolar lines of the two images parallel and aligned. The matching or correlation then consists in matching in the stereoscopic images the homologues or stereo- corresponding, that is to say the projections in the image planes of the same point of the physical surface. The difference between the coordinates of the stereo-correspondents is called disparity. Once the disparities are calculated, the spatial coordinates of the corresponding points of the surface are then calculated by triangulation, the result of the calculations providing a cloud of points located precisely in space. From a cloud of points, mesh techniques make it possible to obtain a continuous surface. N003 B004 FR_recalage_texture_TQD Image correction is a well-known problem, for which powerful and robust algorithms have existed for some time now, cf. for example P. Lasserre and P. Grandjean, "Stereo improvements", in IEEE International Conference on Advanced Robotics, Barcelona, 1995. Similarly, once the mapping is done, the triangulation calculations do not pose any particular problems. On the other hand, the problem of matching is much more complex, and is currently the subject of much research to achieve efficient, fast and robust solutions. For an overview of some mapping techniques (and, incidentally, a better understanding of epipolar geometry in stereovision), cf. for example V. Lemonde, "Stereovision on vehicle: from Self-Calibration to Obstacle Detection", Thesis, CNRS / INSA, November 2005, S. Chambon, "Stereoscopic mapping of color images in the presence occultations ", Thesis, University Paul Sabatier, December 2005, and W. Souid-Miled," Stereoscopic mapping by convex variational approaches ", Thesis, Paris-Est University / INRIA, December 2007. The complexity of the implementation match increases with the number of points you want to match. Indeed, while it is relatively simple to select a small number of points of interest (for example, located in a highly contrasting environment making it possible to easily detect the corresponding stereo-speakers), a denser pairing quickly encounters difficulties of analysis because of the presence of ambiguities in certain areas of the images which sin for example by a too homogeneous aspect, or even by the total absence of content (for example due to occultation phenomena which do not fail to appear, especially for surfaces with pronounced reliefs). Also, the known solutions generally make a compromise between the two following objectives a priori contradictory: the reliability of the mapping and the density of the points clouds obtained.

Mismatch errors result in ob- viously outliers that must be eliminated through filtering techniques. Apart from the fact that this elimination requires power and computation time, it reduces the total number of useful points and thus the density of the reconstruction. As for the low density of point clouds, it is generally compensated by repeating for the same surface the sequence of operations described above, from the acquisition until obtaining a new cloud of points. Given the movements of the scanner (voluntary or involuntary) between two successive shots, the point clouds are shifted spatially. Also, before merging to obtain a single cloud of higher density, it is necessary to recalibrate them spatially. A known solution of the aforementioned document WO 2006/094409 consists in sticking on the surface to be digitized positioning tablets, thanks to which the relative positions of the surface to be scanned and the scanner are known. This technique is interesting but it suffers from a double disadvantage: on the one hand, it requires to instrument the surface to be digitized, which is tedious and is only suitable for surfaces of a certain extent and some outfit; on the other hand, it induces occultations, the areas of the surface located under the pellets not being digitized.

The aim of the invention is to propose a solution that overcomes the aforementioned drawbacks and makes it possible to reliably and densely reconstruct a three-dimensional numerical model of a surface. For this purpose, the invention proposes a method of constructing a three-dimensional digital model of a physical surface, comprising the following operations Projection on the surface of a structured light pattern, Acquisition and storage of a first pair of images two-dimensional stereoscopic, so-called structured, of the illuminated surface, Acquisition and storage of a second pair of two-dimensional, so-called textured, stereoscopic images of the unlit surface; Construction of the digital model from the images. The pair of structured images makes it possible to carry out the matching and to obtain a cloud of points. The couple of textured images can reliably identify points of interest

N003 B004 FR_recalage_texture_TQD will then be identified on a pair of textured images of a next shot, so as to allow a reliable registration of the two successive points clouds. In practice, the method comprises: repeating the acquisition operations to obtain at least a first quadruplet of images and a second quadruplet of images; constructing at least a first and a second successive point clouds from the pairs of structured stereoscopic images of successive quadruplets; the recalibration of point clouds from the stereoscopic pairs of textured images. The registration of the second scatterplot with respect to the first scatterplot is accomplished by a transformation applied to the second scatterplot and comprising a rotational component and a translational component. The rotation component is preferably calculated from data provided by an inertial unit embedded in an apparatus comprising a pair of optical sensors by means of which the stereoscopic images are made.

As for the translation component, its calculation includes, for example: The selection of a set of points of interest in a textured image of the first quadruplet, The determination of the homologues of these points in the corresponding textured image of the second quadruplet, La reinjection of the points of interest into the corresponding structured image of the first quadruplet and the determination of the corresponding stereo-images in the other structured image of the same pair; - The reconstruction of a first cloud of points of reduced density; Reinforcing the homologues of the points of interest in the corresponding structured image of the second quadruplet and determining the corresponding stereo-images in the other structured image of the same pair; - The reconstruction of a second cloud of points of reduced density;

N003 B004 EN_recalage_texture_TQD Applying the inverse of the rotation component to the second scatterplot of reduced density to rotate it in rotation on the first scatterplot of reduced density; Calculating a distance between the first cloud of points of reduced density and the second cloud of points of reduced density, recalibrated in rotation. This distance calculation comprises, for example: calculating the distances separating the points of the first cloud of points of reduced density from the corresponding points of the second cloud of points of reduced density; The calculation of the average distance. The invention also proposes a computer program product comprising instructions for carrying out the operations of the method described above, as well as a three-dimensional scanning system comprising a stereovision system and a central processing unit on which is implemented this program. Other objects and advantages of the invention will become apparent in the light of the description given hereinafter with reference to the accompanying drawings in which: FIG. 1 is a perspective view illustrating a three-dimensional scanning device comprising a capture apparatus connected to a computer, applied to the scanning of the surface of an object, in this case the face of a statue; Figure 2 is a schematic side view illustrating the main optical components of the apparatus of Figure 1, placed in a shooting situation; Figure 3 is a plan view of a pattern for projection on a surface to be digitized, according to a first embodiment; Figure 4 is a detail view of the pattern of Figure 3; FIG. 5 is a plan view of a pattern for projecting onto a surface to be digitized, according to a second variant embodiment; Figure 6 is a detail view of the pattern of Figure 5; N003 B004 FR_recalage_texture_TQD Figure 7 is a plan view of a pattern for projecting onto a surface to be digitized, according to a third embodiment; Figure 8 is a detail view of the pattern of Figure 7; Figure 9 is a plan of a pattern for projection on a surface to be digitized, according to a fourth embodiment; Figure 10 is a detail view of the pattern of Figure 9; Figure 11 shows a pair of structured stereoscopic images of the pattern of Figures 3 and 4 projected onto the face of Figure 1; Fig. 12 shows a pair of textured stereoscopic images of the face of Fig. 1; Fig. 13 is a perspective view of a reconstructed point cloud from the pair of structured stereoscopic images of Fig. 11; FIG. 14 is a perspective view showing two non-recalibrated point clouds, reconstructed from two pairs of structured stereoscopic images captured during two successive shots of the face of the statue of FIG. 1; FIG. 15 is a view similar to FIG. 14, showing the two recalibrated point clouds; FIG. 16 is a perspective view showing a plurality of recalibrated point clouds, reconstructed from a series of pairs of stereoscopic images captured during a sequence of successive shots of the face of the statue of FIG. 1 FIG. 1 shows a contactless optical digitization system 1 for constructing a three-dimensional numerical model, in the form of a computer-assisted (CAD) or computer-assisted design (CAD) environment image, of a physical surface 2, for example the envelope of a real physical object 3. In this case, this object 3 is a statue but it could be any other object having one or more surfaces to be digitized. Generally, the surface 2 to be digitized is embossed, that is to say non-planar, but the system 1, as well as the method used described hereinafter, allow a fortiori the digitization of surfaces N003 B004 FR_recalage_texture_TQD planes. (Note that to meet the formal requirements for patent drawings, the photograph of the statue in Figure 1 has been filtered and converted to two-color display, hence its granular appearance).

The scanning system 1 comprises an optical acquisition device 4 (called capture), called a scanner, equipped with a portable box 5 provided with a handle 6 for its capture and manipulation. The handle 6 carries, on a front face, a manual trigger 7 whose operation commands the shooting.

The system 1 also comprises a central processing unit (CPU or CPU) of the images captured by the scanner 4, in the form of a processor on which is implemented a software application for building the digital models from the captured images. More specifically, this software application includes instructions for carrying out the calculation operations described below. The processor can be embedded in the scanner 4 or, as illustrated in FIG. 1, relocated by being integrated in a computer 8 equipped with a graphical interface and connected to the scanner 4 via a communication interface 9 wired (or wireless), for example in the form of a USB-type computer bus. The scanner 4 is designed to work in stereovision. As can be seen in FIGS. 1 and 2, it comprises, mounted in the housing 5: a light projector 10; a stereovision system 11 comprising a pair of optical acquisition devices 12, 13 of the camera type (that is to say designed to take continuous images, for example at the normalized frame rate of 24 frames per second ), or cameras (that is to say shooting occasionally, possibly burst); an optical sighting system 14; an inertial unit 15, configured to provide at any time an invariant absolute reference, constructed from

N003 B004 EN_recalage_texture_TQD two orthogonal directions identifiable by the central unit 15, namely magnetic north and vertical. It is assumed in the following that the optical acquisition devices 12, 13 are cameras, which, if necessary, can be used as cameras. The projector 10 comprises a light source (not visible), a focusing optics 16 and a slide (not visible) interposed between the light source and the focusing optics 16. The light source is preferably a non-coherent source of white light, in particular of the filament or halogen type, or also of the diode (LED) type. The focusing optics 16 of the projector 10 define a main optical axis 17 passing through the light source. This optical axis 17 defines an optical axis of projection of the scanner 4.

The cameras 12, 13 each comprise: a focusing optics 18 defining an optical axis 19 (respectively 20) of sight, a photosensitive sensor 21, for example of the CCD type, which is in the form of a square or rectangular plate placed, facing the focusing optics 18, on the optical axis 19, 20, that is to say perpendicular thereto and so that the axis 19, 20 passes through the center of the sensor 21. optical centers of the cameras 12, 13 are spaced from each other by a distance called base, their optical axes 19, 20 converging towards a point 22 located on the optical axis 17 of the projector 10, in the field of the -this. In practice, the cameras 12, 13 will be oriented such that the point of convergence of their optical axes 19, 20 lies in a median plane situated in the projection field and in the sharpness zone (also called depth of field) 10. The sensor 21 of each camera 12, 13 is placed at a predetermined distance from the focusing optics 18, in an image plane depending on its focal length and such that the object plane corresponding to this image plane is in the image plane. Sharpness area of the projector 10.

As illustrated in FIG. 2, the projector 10 and the cameras 12, 13 are fixed on a common unitary plate 23, made of a sufficiently rigid material to minimize the risks of misalignment of the cameras 12, 13 (such misalignment is called scaling). In practice, the plate 23 is made of an aluminum alloy. As can be seen in FIG. 1, the handle 6 of the scanner 4 extends parallel to the base, so that the natural hold of the scanner 4 is vertical, the cameras 12, 13 extending one above the other. the other. By convention, however, the left camera is referred to as the upper camera 12 (located on the thumb side) and the right camera as the lower camera 13.

The aiming system 14 is arranged to allow, prior to the shooting, a positioning of the scanner 4 at a distance from the surface 2 to be digitized such that the projected pattern is included in the field of the projector 10 - and therefore net . As illustrated in FIG. 2, the aiming system 14 comprises two laser pointers 24 rigidly mounted on the plate 23, designed to emit each, when the trigger 7 is depressed, a linear light beam 25, 26 producing on any surface illuminated, a light spot substantially punctual. The pointers 24 are positioned angularly on the plate 23 so that the beams 25, 26 emitted cross at a point of intersection 27 located on the axis 17 of the projector 10, in the sharpening zone thereof and upstream of the point 22 of convergence of the optical axes 19, 20 of the cameras 12, 13. The slide is disposed between the light source and the optical 16 of the projector 10, on the axis 17, that is to say perpendicular to the axis 17 and so that it goes through the center of the slide. The slide is placed at a predetermined distance from the optics 16 of which it constitutes an object plane, so that the image of the pattern is sharp in an image plane perpendicular to the axis 17. In practice, the image plane n ' is not unique, the focusing optics of the projector 10 being designed (or adjusted) to have a certain depth of field, such that the image of the projected pattern is sharp in a certain area of sharpness that extends between two spaced planes perpendicular to the axis 17.

The slide is in the form of a translucent or transparent plate (glass or plastic), square or rectangular, on which the pattern is printed by a conventional method (transfer, offset, serigraphy, flexography, laser). , inkjet, microlithography, etc.). The pattern is structured, that is to say it has alternations of light areas and dark areas of a predetermined shape. The constitution of the pattern (color, contrast) is not random: it is known in every respect. The pattern can be done in grayscale or color, but is preferably made in black and white, maximizing the contrast between light and dark areas. More precisely, the pattern comprises the combination of two sub-patterns, namely: A regular primary pattern comprising an array of spaced, continuous rectilinear fringes, and a secondary generally anisotropic pattern, i.e. irregular and non-repetitive in any direction of space.

FIGS. 3 to 10 show four variants of patterns that can be used in the digitization system. According to an embodiment illustrated in Figures 3 to 8 which illustrate three variants, the secondary pattern 30 comprises scabs 31 extending in the spaces between fringes 29. According to another embodiment illustrated in Figures 9 and 10, the secondary pattern 30 includes pictograms arranged pseudo-randomly. A scabby secondary pattern as shown in FIGS. 3 to 8 can be generated automatically by means of a computer program for performing pseudorandom patterns. One example is Ken Perlin's algorithm generating a well-known type of mouchetis called Perlin noise, which has the advantage of being anisotropic. In detail, this type of pattern includes an interweaving of black and white spots that give a grainy appearance to the pattern. The term "scab" refers to this interlacing.

In English, this type of pattern is also called "Speckle". As will be seen in more detail below, the primary pattern 28 has the function of locally generating a strong, easily detectable contrast between white light areas and dark areas, while the secondary pattern 30 functions by its anisotropic nature. , to locate in the image with certainty any viewing window. In the first variant of the first embodiment, shown as a whole in FIG. 3 and in detail with scale N003 B004 EN_recalage_texture_TQD enlarged in FIG. 4, the primary pattern 28 is single-oriented and comprises a series of clear fringes 29 (white). in this case) which extend vertically in the orientation illustrated in Figures 3 and 4, each bordered on either side by two parallel strips 32 (black in this case) parallel, similar. The width of the fringes 29 is constant, as well as the spacing between the fringes 29. As can be seen in FIG. 3, and in more detail in FIG. 4, the scabs 31 of the secondary pattern 30 extend in strips through the fringes. spaces between fringes 29, and more precisely between the black bands 32 bordering the white fringes 29. Given the mono-directional nature of the primary pattern 28, there is a preferred orientation of the general pattern, relative to the positioning of the stereovision system 11. Indeed, the image is printed on the slide where it is oriented so that the fringes 29 extend perpendicularly to the base, that is to say to the axis connecting the optical centers of the cameras. 12, 13. In other words, the fringes 29 extend perpendicular to the plane containing the optical axes 19, 20 of the cameras 12, 13, so that in stereoscopic vision the fringes 29 extend vertically on the images captured by In any case, the presence of clear fringes 29, bordered with black bands 32, makes it possible to provide the pattern with contrasting areas that can be identified during image processing, as we will explain more. in detail below. This type of pattern is advantageous in comparison with a simple speckle which appears to be of low contrast, since the black and white spots of the slide would, by projection, form grayscale spots. As for an isotropic pattern made for example only fringes, it is inherently unsuitable for stereovision because it is not localizable and therefore unsuitable for matching. In the second variant, shown as a whole in FIG. 5 and in enlarged detail in FIG. 6, the primary pattern 28 is bi-oriented and comprises a series of light fringes 29 (white in this case) which extend both horizontally and vertically to form a grid. Each horizontal and vertical fringe 29 is bordered on both sides by discontinuous black (black) tapes, which together form a network of regularly spaced isolated squares with black edges. As can be seen in FIG. 5, and in more detail in FIG. 6, the scabs 31 of the secondary pattern 30 extend in blocks in the spaces between the fringes 29, that is to say in the squares delimited by the This second variant of the pattern has, compared to the first variant, the advantage of being bi-oriented, so that there is no preferred direction of projection of the pattern on the surface 2, by In addition, the multiplication of the fringes 29 makes it possible to increase the density of the contrasting zones of the pattern, in this case at the level of the fringes 29. In the third variant, represented as a whole on the FIG. 7 and in enlarged detail in FIG. 8, the primary pattern 28 is bi-oriented and comprises a series of dark fringes 29 (black in this case) which extend both horizontally and vertically to form a grid . Each horizontal and vertical fringe 29 is bordered, on both sides, by discontinuous light strips 32 (white in this case) which together form a network of regularly spaced isolated squares with white edges. In addition, the edges 32 of each square are lined, inward, with black bands 33, so that each square has a double perimeter, consisting of a white outer perimeter and a black inner perimeter. In addition to having the same benefit as the second variant of being bi-oriented, this third variant also has the advantage of further increasing the density of contrasting areas of the pattern. In the second embodiment, shown as a whole in Fig. 9 and in enlarged detail in Fig. 10, the primary pattern 28 is bi-oriented, and comprises a series of light fringes 29 (white in this case). which extend both horizontally and vertically to form a grid. The squares delimited by the fringes 29 have a dark background (black in this case) on which stand out pictograms 34 of light color (white in this case). As can be seen in FIG. 9, only a limited number of different pictograms 34 can be used. However, in order to make the secondary pattern 30 N003 FR_recalage_texture_TQD globally anisotropic, the pictograms 34 are distributed pseudo-randomly and so that the neighborhood of each pictogram 34 (for example the adjacent pictograms 34 located on the same line, or on the same column) is different from the neighborhoods of all the other pictograms 34. This pattern has the advantage of being more contrasted than the scab patterns, which facilitates matching operations (see below). The operation of the three-dimensional scanning system 1 is now described. This operation is based on the succession of the following steps, performed after a prior calibration step of the stereovision system 11: Digitization of the surface 2; Rectification of images; Reconstruction of point clouds; - Recalibration of point clouds; - Construction of the three-dimensional model.

Calibration Calibration of the stereovision system 11 is an indispensable preliminary step, without which it is not possible to properly process the information captured by the cameras 12, 13. The calibration of a single camera (in monocular vision) is limited to estimate the intrinsic parameters of the camera as well as its position relative to an absolute reference. The calibration of the stereovision system 11 is more complex because it involves performing a double calibration, which includes not only the estimation of the intrinsic parameters of each camera 12, 13, but also the relative position and orientation of the cameras 12 , 13 relative to each other. In practice, the calibration consists in matching the two cameras 12, 13 by implementing in the digitization system CPU 1 a given number of structural and optical parameters of the stereovision system 11, in particular the position of the optical centers and the angle between the optical axes 19, 20. This calibration is not necessarily performed before each use of the system 1. It is preferably made in the factory, using a test pattern (for example a checkerboard) whose geometry is known with accuracy. N003 B004 EN_recalage_texture_TQD The stereovision system 11 can be calibrated according to a known procedure and algorithms. For this purpose, the skilled person may in particular refer to V. Lemonde, "Stéréovision embedded on vehicle: from self-calibration to the detection of obstacles", Thesis, CNRS / INSA, November 2005, above, or to B. Bocquillon, "Contributions to the autocalibration of cameras: models and solutions guaranteed by interval analysis", Thesis, University of Toulouse, October 2008.

Scanning Scanning is the step of acquiring one or more pairs of stereoscopic images of the surface 2. More precisely, the scanning procedure comprises the succession of the following operations.

In the first place, the scanner 4 is positioned facing the surface 2 to be digitized - in this case facing the face of the statue. A pulse on the trigger 7 causes the lighting of the pointers 24. The scanner 4 can then be positioned correctly, the concordance of the light spots produced by the lasers on the surface 2 signifying that it is located in the field of the projector 10 and 12, 13. Once positioned the scanner 4, the acquisition procedure is then initiated by a long press on the trigger 7. The procedure continues as long as the trigger 7 is held down. This procedure comprises the following sequence of steps: a.1) Lighting the projector 10. This lighting causes the projection of the pattern on the surface 2. a.2) Switching off the pointers (to avoid the blindness of the cameras 12, 13) and acquisition by the cameras 12, 13 of a first pair of two-dimensional stereoscopic images of the surface 2 thus illuminated. These images are said to be structured because they contain the structured pattern projected on the surface 2, according to the two different points of view of the cameras 12, 13. Although the system 11 of stereovision is not necessarily oriented horizontally, it is conventionally referred to as a left-hand image. and the structured image produced by the left camera 12, and the right image - denoted by D - the structured image produced by the right camera 13 are noted. N003 B004 EN_recalage_texture_TQD The acquisition of an image comprises in fact two steps . First the shooting, during which the image is printed on the photosensitive cells of the sensor 21, then the reading of the image by the on-board electronics of the camera 12, 13, with progressive setting of the image, in digital form, in a buffer embedded in the camera. The sensors 21 being blocked during their reading, the cameras are momentarily unusable and therefore put on hold. a.3) Memorization of the G-D structured stereoscopic image pair: once the G and D images have been buffered, they can be transmitted to the system CPU 11 where they are stored for processing ( see the rectification and reconstruction phases described below). A pair of structured stereoscopic images GD of the face 2 of the statue 1 is illustrated in FIG. 13. This figure is provided for illustrative purposes, the GD images not necessarily being displayed on the graphical interface of the computer 8. a.4) Shutdown of the projector 10. This shutdown can be controlled immediately after the acquisition of the structured images G and D, for example during the reading of the images, or during their storage by the CPU of the system 11. a.5) Acquisition by the cameras 12, 13 of a second pair of two-dimensional stereoscopic images of the unlit surface 2 (and not dotted). This operation is similar to the operation a.2), except that the images are not structured, the pattern has not been projected onto the surface 2. The images of the unlit surface 2 are made in natural light (ambient), optionally embellished with a white light produced for example by light-emitting diodes (LEDs) equipping the scanner 4. Thus, according to a particular embodiment, the scanner 4 is equipped in front of two circular series of LEDs surrounding the optics 18 of the cameras 12, 13 and oriented along the axes 19, 20 thereof. These images are said to be textured, because in the absence of the projected motif they contain the natural texture of the surface 2. The left image is denoted G '; the right image D '. The images G 'and D' are read by the internal electronics of the cameras 12, 13 and stored in N003 B004 EN_recalage_texture_TQD buffer. Meanwhile, the cameras 12, 13 are put on hold. a.6) Memorization of textured images G'-D '. Once the images G 'and D' are buffered, they are transmitted to the CPU of the system 11 where they are stored for processing with the pair of G-D structured stereoscopic images. A pair of textured stereoscopic images G'-D 'of the face 2 of the statue 1 is illustrated in Fig. 14. (NB to meet the formal requirements for patent drawings, the images shown in Fig. 14 have been filtered and converted for display in two colors, hence their granular appearance not to be confused with scab). This acquisition procedure therefore provides a quadruplet of images, hereinafter referred to as "quad", composed of the two pairs of stereoscopic images: a pair of structured images GD and a pair of textured images G'-D ' . The procedure, ie the sequence of operations a.1) to a.6), can be repeated as much as necessary, in order to obtain a series of quads. During this rehearsal, it is possible to scan the surface 2 to cover it completely and shoot it continuously. In order to increase the density of the reconstruction (see below), it is also preferable to make several acquisitions from the same point of view, or from similar points of view in order to obtain an overlap of the zones of the surface 2 covered by the cameras 12, 13. In order to avoid the drift of the scanner 4 out of the field of the projector 10 during the scanning of the surface 2, the pointers 24 can be activated permanently - with the exception of the sequences of shots otherwise the images would be unusable - so as to allow the user to keep the distance between the scanner 4 and the surface 2 relatively constant. In view of the camera shutter speeds 12, 13 order of a few milliseconds), the eye does not perceive the extinction of the lasers 24 during the shooting, the user having the illusion of a continuous pointing.

Given the accuracy of the CCDs and the memory capacity of the system 1, conclusive tests could be conducted with the following parameters: N003 B004 FR_recalage_texture_TQD - Frequency of the acquisition (1 quad): 4 Hz (4 quads / s) ) Shutter speed (shooting time): approx. 5 ms Playback time for each frame: 28 to 50 ms CCD sensor size: 1024x768 pixels 8-bit gray scale (0-255) - Memory space required for each image: 0.7 MB

Rectification The raw images of each quad, obtained by the acquisition procedure described above, are affected by defects, notably a distortion and an absence of parallelism of the epipolar lines between images of the same stereoscopic pair. Also, before submitting the images to the processing to allow the reconstruction of the three-dimensional model of the surface 2, is it preferable to apply to them a pretreatment including in particular the following operations, programmed in the form of instructions in the CPU of the computer 8 for their implementation: - Rectification of the distortion (for example by means of known algorithms), aimed at straightening the straight lines which appear curved on the images; Epipolar rectification, which allows, for a pair of stereoscopic images, to match at any point of the left image a horizontal line of the right image, and vice versa. Like the distortion rectification, epipolar rectification can be conducted using conventional algorithms. The skilled person can refer to the documents mentioned in the introduction.

Reconstruction The reconstruction step aims to calculate, from the pair of G-D structured stereoscopic images, a three-dimensional point cloud of the surface to be digitized. The reconstruction comprises two phases, programmed in the form of instructions in the CPU of the computer 8 for their implementation: The mapping (MEC) The triangulation.

N003 B004 EN_recalage_texture_TQD Mapping The MEC consists of matching points from the G-D structured image pair. The MEC is performed in two stages: an initial MEC, relatively coarse; then a final, finer MEC, based on the results of the initial MEC. The initial MEC consists in performing for each pixel PG of coordinates (i, f) of the image G a local correlation consisting of: - Selecting in the image G a neighborhood, called a correlation window, centered on the pixel PG; Compare the correlation window of the PG pixel with a series of correlation windows of the same size (in the so-called Block matching methods) or of different size centered on a PD pixel of coordinates (k, l) of the image D, located on the line of the image D containing the epipolar line corresponding to the pixel PG; each correlation window of the image D corresponds to a correlation measure (or score); Select in this series the correlation window that maximizes the correlation score - or minimizes it, depending on whether similarity or dissimilarity criteria are used. The pixel PD located in the center of this window is then matched to the pixel PG, the pixels PG and PD being called homologous or stereo-corresponding. We can then calculate a first estimate, called initial, of the disparity D (x) between the views 1 and 2 at the pixel PG: D (PG) = x (PD) ûx (PG)

Where x is a measure parallel to the horizontal lines of the G and D (abscissa) images.

The initial MEC is relatively robust. It is all the more so because the secondary pattern 30, pseudo-random, makes it possible to minimize mismatches. It would be possible, from the coordinates x (PG) and x (PD) of the stereo-corresponding pixels, and thanks to a calculation of triangulation, to calculate the position of a point of the surface 2 whose projections on the images N003 B004 FR_recalage_texture_TQD G and D would be located respectively in the PG and PD pixels. However, the accuracy of the positioning of this point, particularly in depth, would be insufficient to meet the objectives set by the applicant, namely a depth tolerance of 0.1 mm.

Indeed, disparity and depth accuracy can be related by the following equation:

Az = Ztravaii2. Focal-base AD Where Oz (expressed in mm) is the depth tolerance, measured along the optical axis 17 of the projector 10; Work (expressed in mm) is approximately the normal working distance of the scanner 4, i.e. the average distance between the scanner 4 and the surface 2 to be digitized, measured along the optical axis 17 of the projector 10 ; focal length (expressed in pixel equivalents) is the focal length of the cameras 12, 13; base (expressed in mm) is the distance between the optical centers of the cameras 12, 13; AD (expressed in pixels) the tolerance on disparity. It is assumed that the system 11 has the following configuration: Z Work between 350 and 500 mm (425 mm on average) focal length 1700 pixels base 140 mm.

Given this configuration (which can of course vary), and assuming that it is desired to achieve an accuracy of 0.1 mm Oz, the accuracy of the disparity must be 0.1 pixel. In other words, the disparity must be precise to the sub-pixel, and more precisely to the tenth of a pixel. Such precision is not attainable by means of an initial MEK as described above. However, as we will see now, the results of the initial MEC can be used as a basis for the fine MEC, which is more accurate but based mainly on the points of the primary pattern 28. N003 B004 EN_recalage_texture_TQD (1) The Final ECM includes the following operations, programmed as instructions in the CPU of the computer 8 for their implementation: b.1) Detection, in the image G, of local peaks of light intensity.

They may be vertices or valleys, which are formally equivalent. In practice, the vertices are detected. This detection can be carried out using known algorithms, such as the algorithm of Biais and Rioux (see F. Biais and M. Rioux, "Real-time numerical peak detector", in Signal processing, 1986, vol. 11, No. 2, pp. 145-155). Concretely, from the image G, we establish a map of a derivative in each pixel PG (i, j) of the light intensity corresponding to this pixel. Insofar as the luminous intensity is not a continuous but discrete function, the derivative is computed in a discrete manner, for example equal to the slope of the segment joining two successive points whose abscissa values are those of the corresponding pixels, and the ordinates the values of the corresponding light intensities (on a scale from 0 to 255 for eight-bit gray levels).

It is then decreed that a pixel corresponds to a peak when a function derived from the luminous intensity changes sign between this pixel and the neighboring pixel situated on the same line PG (i + 1, j). More precisely, and depending on the chosen algorithm, we can decree that the pixel corresponds to a peak when the function derived from the luminous intensity changes from sign to rising, that is to say that it is negative to the pixel PG (i., j) and positive at the pixel PG (i + 1, j). b.2) Calculation of the coordinates of the points of the image G, called extremums (in practice it is about maximums) and noted here EG, corresponding to the detected peaks. These points are not pixels. These are points whose coordinates in the image G are real numbers. For this purpose, for a peak detected in the pixel PG (i, j) during the operation b.1), a window containing the pixel PG (i, j) is chosen, for example the window comprising PG (i , j) and the two pixels framing it on the same line, PG (i-1, j) and PG (i + 1, j).

N003 B004 FR_recalage_texture_TQD We then calculate a continuous interpolation function of the luminous intensity of the pixels of this window, then we calculate the abscissa XG of the extremum EG, that is to say of the point which maximizes this function of 'interpolation. The interpolation function being continuous, the abscissa XG is not complete: it is a real number. b.3) For each extremum EG thus calculated, determination, in the second D image of the torque GD, of the corresponding stereo of the pixel PG (i, j), that is to say of the pixel PD (k, l) matched, at the initial MEC, to the pixel PG (i, j) of the image G containing the extremum EG. b.4) Selection, in the image D, of a window containing the pixel PD (k, l). This window comprises for example PD (k, l) and the pixels flanking it on the same line, ie PD (k -1,1) and PD (k + 1, l). b.5) Detection in this window of a peak of light intensity. b.6) If such a peak exists, calculation of a continuous interpolation function of the luminous intensity of the pixels of this window, and calculation of the abscissa XD of the extremum ED, that is to say of the point that maximizes this interpolation function. As for the extremum EG, the abscissa of the extremum ED is not complete since it is a real number. b.7) Matching of the extremes EG and ED. We can possibly calculate the disparity: D = XD ùXG The abscissa XD and XG being, as we have seen, real numbers, the precision obtained on the disparity is well below 0.1 pixel, in accordance with the objectives indicated below. above. Steps b.1) to b.7) are repeated for all light intensity peaks, the mapping produces a set of pairs of points EG and ED matched for the G-D image pair. As we have seen, the contrast is lower in the secondary pattern 30 than in the primary pattern 28. Also the detection of peaks is less reliable in the areas of the images G and D corresponding to the secondary pattern 30, than in the zones corresponding to the primary pattern 28, in which the alternation of the light and dark areas is clearer. N003 B004 EN_recalage_texture_TQD Also, for precision purposes - but at the expense of the reconstruction density ù, is it preferable to limit the fine ECM to a selection of points of interest located in the fringes 29 of the primary pattern 28, that it is therefore necessary to locate. This location can be performed by vertical scanning of the image (in the case of the first pattern version) or by combining a vertical scan and a horizontal scan (in the case of the second version and the third version of the pattern), and by estimating the similarity of the neighborhoods. The fringes 29 are characterized in fact by a directional isotropy which results in a homogeneity of the neighborhoods of the points of the fringes 29 in the direction in which it extends.

Trianqulation The extremums EG and ED being homologous (or stereo- corresponding), they are the projections, respectively in the left image G and in the right image D, of the same point of the surface 2 to be digitized. Therefore, the coordinates of this point can be easily calculated by triangulation from the positions of EG and ED.

Thus, from the set of doublets of extremes EG and ED, the triangulation makes it possible to obtain a cloud of points which forms a first blank, scattered, of the three-dimensional model of the surface to be digitized. Such a cloud of points is represented in FIG. 15. This cloud results from the succession of the MEC and triangulation phases applied to the image pair GD of FIG. 11. It can be seen from FIG. 13 that the cloud is oriented, and includes several groups of points all located in vertical parallel planes. This illustrates the MEC technique described above, in which the fine MEC has been limited to a selection of points of interest located in the fringes 29 of the primary pattern 28, due to the accuracy of their location in the G images and D.

Recalibration As the reconstruction is repeated for all the GD structured image pairs captured during the multiple shots of the surface 2, we obtain a series of scattered point clouds without any link between them, shifted in space. , that it is therefore necessary to recalibrate to merge them before starting the meshing procedure for constructing the three-dimensional model of the surface 2. There is shown in FIG. 14 two point clouds reconstructed from two successive quads, before registration. The resetting operations are programmed as instructions in the CPU of the computer 8 for their implementation. From the point of view of the cameras 12, 13, the scanned surface 2 has evolved in space, between two successive shots, in a reference linked to the scanner 4, in practice a Cartesian landmark whose center coincides with the image focus of the left camera 12, and whose axes are respectively: x: the line parallel to the horizontal of the sensor 21 of the left camera 12 passing through its focus image, located in the center of the sensor 21; y: the line parallel to the verticals of the sensor 21 of the left camera 12 and passing through its image focus; z: the optical axis 19 of the left camera 12. The registration aims to evaluate the movements of the scanner 12 between two successive shots, then to apply to the point clouds resulting from these shots transformations (called "poses" and each constituted by the combination of a rotation and a translation) corresponding to these displacements, in order to align the marks of each point cloud.

Data from: the inertial unit 15 of the scanner 4 is used for this purpose, which, as we have seen, provides at all times the magnetic north and the vertical and therefore behaves like a three-dimensional compass; the pair of textured stereoscopic images G'-D '.

Calculation of the rotation One uses for the computation of the rotation of the data coming from the inertial unit 15. Let a pair of successive points clouds (Ni, N1 + 1), allowing to reconstruct two clouds of respective points. It is desired to calculate the relative rotation of the scanner 4 between Ni and Ni + 1, in order to

N003 B004 EN_recalage_texture_TQD to apply the inverse of this rotation to the second cloud to set it in rotation on the first one. The data of the inertial unit 15 makes it possible to calculate: the absolute rotation matrix, denoted Ri, transformation matrix of the reference associated with the scanner 4 towards the terrestrial reference at the iteration i, the absolute rotation matrix, denoted Ri + 1, transformation matrix of the repository associated with the scanner 4 towards the terrestrial repository at the i + 1 iteration.

The relative rotation matrix between the cloud Ni and the cloud Ni + 1, denoted R + 1 is equal to the product of the matrix Ri by the inverse matrix of the matrix Ri + 1 R1 + 1 = Ri-11. Ri We apply the inverse of this rotation to the points of the cloud N; +1 to set it in rotation on the Ni cloud.

Calculation of the translation The scanner 4 having been moved between the two successive quads corresponding to the Ni and Ni + 1 clouds (for an acquisition frequency of 4 Hz, there is in practice a time shift of approximately 250 ms between the quads), the successive pairs of structured stereoscopic images Gi-Di and Gi + 1-Di + 1, on the basis of which the clouds were reconstructed, are unusable only for the calculation of the translation between clouds.

Indeed, the projected pattern has moved on the surface 2 to be digitized, so that no reliable correlation can be made directly between the left images Gi and Gi + 1 (respectively between the straight images Di and Di + 1) pairs of images Gi-Di and Gi + 1-Di + 1 Also, for the calculation of the translation, are we using the pairs of stereoscopic textured images G'i-D'i and G'i + 1 -D'i + 1 quads. We begin by selecting in G'i a set of points of interest (in practice between 200 and 300), by means of a filtering algorithm for detecting singularities. Then we search in G'i + 1 for the corresponding points of G'i, by means of a point tracking algorithm (for example N003 B004 FR_recalage_texture_TQD the Lucas-Kanade point-tracking algorithm, see B. Lucas and T. Kanade, "An Iterative Image Registration Technique with an Application to Stereo Vision," in Proceedings of Imaging Understanding Workshop, pp. 121-130, 1981) based on surface texture similarities of 2, acquired at the same time. acquisition of G'-D 'textured images (hence the usefulness of these images as part of the registration). We thus have two sets of points: points of interest of G'1 matched in G'1 + 1 points of G'i + 1 matched at points P1G 'We then reinject the points PPG' in the structured image Gi rectified , and for each of these points, an initial MEC is performed (or the results of the initial MEC already performed for these points, see supra) are reused with the image Di of the same stereoscopic pair G1-Di, and then a reconstruction is carried out to obtain a first cloud of points of reduced density, expressed in the reference associated with the cloud Ni and comprising a number of points less than this. The same operation is carried out for the points P1 + i in the stereoscopic pair Gi + 1-Di + 1 so as to obtain a second cloud of points of reduced density, expressed in the reference associated with the cloud N1 + 1 and deemed to be the evolution in the space of the first cloud of points of reduced density. This reinjection supposes either to have first rectified the textured images G'1 and to rectify the points PPG 'and Pi + i, so as to correspond at least locally the landmarks of the textured images with those of the structured images in which the points are reinjected. The inverse of the rotation R + 1 is then applied to the second cloud of reduced density in order to set it in rotation on the first cloud of reduced density.

Then, for each point PpG 'of the first cloud of reduced density, the distance separating it (in space) from its corresponding Pi + i in the second cloud of reduced density is calculated. The average of these distances is extracted, which one decrees equal to the translation undergone by the cloud of points N1 + 1 with respect to the first Ni.

Each pose, which includes a rotation and a translation, is thus complete. We then apply the inverse of this pose to N003 B004 FR_recalage_texture_TQD pG '' Pi + Gl the set of points of the Ni + 1 cloud to recalibrate it in translation on the Ni cloud. FIG. 15 shows the clouds of FIG. 14, recalibrated. The operations just described are repeated with the succession of scatter plots, which are thus recalibrated with respect to each other.

Construction of the model The construction of the numerical model of the surface 2 is carried out starting from the clouds of points resulting from the resetting. Since many areas of the object have been captured several times (there are overlapping areas between the successive views), the final point cloud, obtained by aggregating the points clouds resulting from the resetting, has a high density (see figure). 16, which shows ten clouds of successive points recalibrated). We can then perform from the points of the cloud a mesh, to obtain a second model draft consisting of contiguous triangles, then an interpolation calculation allowing from this second draft to reconstruct a true surface model based on functions continuous mathematics, compatible with CAD systems. N003 B004 EN_recalage_texture_TQD

Claims (8)

REVENDICATIONS1. Method for constructing a three-dimensional digital model of a physical surface (2), comprising the following operations: Projection on the surface (2) of a structured light pattern, Acquisition of stereoscopic images of the surface (2), Construction This method is characterized in that the acquisition step comprises the acquisition of a quadruplet of images including: A first pair (GD) of two-dimensional stereoscopic images, called structured images, of the surface (2) illuminated, A second pair (G'-D ') of two-dimensional stereoscopic images, called textured, of the surface (2) unlit. 2. Method according to claim 1, which comprises: repeating the acquisition operations to obtain at least a first quadruplet (G; -D1; G'1-D'1) of images and a second quadruplet (G; + 1-D1 + 1; G ', + 1-D's + 1) of images; constructing at least a first cloud of points (Ni) and a second successive cloud of points (Ni + 1) from the pairs of structured stereoscopic images of successive quadruplets; recalibrating the point clouds (Ni, Ni + 1) from the stereoscopic pairs (G '; D'; G '; + 1-D'; + 1) of textured images. 3. The method according to claim 2, wherein the registration of the second point cloud (Ni + 1) with respect to the first point cloud (Ni) is performed by means of a transformation applied to the second point cloud (N1 + 1). ) and comprising a rotation component and a translation component. The method according to claim 3, wherein the rotation component is calculated from data provided by an inertial unit (15) embedded in an apparatus (4) having a pair of optical sensors (21) by means of which the stereoscopic images. The method of claim 4, wherein calculating the translation component comprises: N003 B004 FR_recalage_texture_TQDThe selection of a set of points of interest (PG ') in a textured image G'; of the first quadruplet (G ;-D;; G '; - D'1), - The determination of the homologues (n1) of these points (Ph in the corresponding textured image (G'i + 1) of the second quadruplet (Gi + 1-Di + 1; G'i + 1-D'1 + 1), the reinjection of the points of interest (PG ') into the corresponding structured image (G;) of the first quadruplet (Gi-Di; G '1-D't) and the determination of the stereo-corresponding in the other structured image (D;;) of the same pair (G ;-D;); - The reconstruction of a first cloud of points of reduced density The reinjection of the homologues (PG'1) of the points of interest (PG ') into the corresponding structured image (G1 + 1) of the second quadruplet (G + 1-Di + 1; Gt + 1-D', + 1) and the determination of the corresponding stereo-images in the other structured image (Di + 1) of the same pair (G; + 1-D- + 1); - the reconstruction of a second cloud of points of reduced density; Applying the inverse of the rotation component to the second scatterplot of reduced density to set it in rotation the first cloud of points of reduced density; Calculating a distance between the first cloud of points of reduced density and the second cloud of points of reduced density recalibrated in rotation. The method of claim 5, wherein the distance calculation comprises: calculating the distances separating the points of the first cloud of reduced density points from corresponding points of the second reduced density dot cloud; - The calculation of the average distance. 7. Computer program product comprising instructions for carrying out the operations of the method according to one of claims 3 to 6. 8. A three-dimensional scanning system (1) comprising a stereovision system (11) and a central processing unit on which the program according to claim 7 is implemented. N003 B004 EN_recalage_texture_TQD