Structured Light Stereoscopic Imaging with Dynamic Pseudo-random Patterns

Transcription

1 Stuctued Light Steeoscopic Imaging with Dynamic Pseudo-andom Pattens Piee Payeu and Danick Desjadins Univesity of Ottawa, SITE, 800 King Edwad, Ottawa, ON, Canada, K1N 6N5 Abstact. Stuctued light steeoscopic imaging offes an efficient and affodable solution to 3D modeling of objects. The majoity of stuctued light pattens that have been poposed eithe povide a limited esolution o ae sensitive to the inheent textue on the suface of the object. This pape poposes an innovative imaging stategy that accomplishes 3D econstuction of objects using a combination of spatial-neighboing and time-multiplexing stuctued light pattens encoded with uniquely defined pseudo-andom colo codes. The appoach is extended with the concept of dynamic pattens that adaptively inceases the econstuction esolution. Oiginal techniques ae intoduced to ecove and validate pseudo-andom codes fom steeoscopic images, and to consistently map colo and textue ove the econstucted suface map. Expeimental esults demonstate the potential of the solution to ceate econstuctions with vaious densities of points and pove the obustness of the appoach on objects with diffeent suface popeties. Keywods: steeoscopic vision, stuctued light, 3D modeling, pseudo-andom pattens. 1 Intoduction The objective of the pesent wok is to develop an integated 3D imaging and econstuction system that opeates fom affodable off-the-shelf equipment, povides the possibility to scan at vaious esolutions on demand, and opeates on a wide vaiety of objects with diffeent eflectance chaacteistics. Seveal techniques can be employed to estimate the shape and visual appeaance of objects. A valid solution should be able to gathe accuate 3D points that can be intepeted eadily, with diffeent density levels, and while only sacificing pocessing time when deemed necessay. Many techniques aguably poduce vey high quality 3D maps but eithe equie a lage amount of pocessing o expensive devices to achieve them. The poposed solution povides entie flexibility between accuacy and pocessing time. It uses active steeoscopic vision and counts on a pojected patten with unique encodings. This ensues a high eliability in the featue matching pocedue and a low dependency on the eflective popeties and colos of the objects. Unde its basic fomulation, steeoscopy usually suffes fom a lack of eliable featue points, especially in man-made envionments, o fom uneliable matching M. Kamel and A. Campilho (Eds.): ICIAR 2009, LNCS 5627, pp , Spinge-Velag Belin Heidelbeg 2009

2 688 P. Payeu and D. Desjadins between the views. As an altenative, active vision methods pefom exceptionally well in aeas whee no featues can be eadily detected, and ensue highe eliability on matches. Beyond time-of-flight solutions that ae suitable ove lage distances [1], o active lase tiangulation that povides vey high esolution but equies expensive devices [2], stuctued lighting emains an affodable but obust stategy. The main diffeence between stuctued lighting appoaches emains in the natue of the pojected patten. The latte can be classified in thee main categoies [3]: timemultiplexing, spatial neighboing, and diect coding. Time-multiplexing pattens ae tempoal codes that ae pojected sequentially ove the scene. The codewod fo a cetain position is composed of the illumination values of a paticula pixel position thoughout all the pattens and is unique fo evey location. Binay Gay codes [4], [5], and n-ay codes that use linea o peiodical vaiation ove the entie ange of intensity values [6], [7], ae well established examples of time-multiplexing appoaches. The sensitivity to extenal lighting level and eflectance popeties emain high, and solutions based on these techniques ae often constained to unifomly shaded sufaces. Spatial neighboing encodes positions as spatially distibuted codes. Because a single patten is pojected, pecision is usually not as high as with the one obtained when employing time-multiplexing pattens. A vaiety of such pattens have been poposed, that encode the alphabet of the sequence in diffeent ways [8], [9], [10]. Pseudoandom aays offe a simplified way to encode spatially distibuted maps that ae uniquely defined. Lavoie et al. [11] encode pseudo-andom gids as bi-dimensional binay aays (PRBA) composed of sequences in which any subset (o window) is unique and defines a specific location. They also extend the technique to pseudoandom multi-valued sequences (PRMVS) whee multiple values ae substituted by colos fom a pedetemined colo palette [12]. Diect codification involves a complete patten that uniquely labels evey visible pixel and is pojected only once. A location is then coded as a pecise intensity level o colo. Caihill and Hummel [13] developed a senso whee a linea wedge speads along columns containing vaying gey levels. A atio is calculated between the intensity values of evey pixel unde the linea wedge and unde a patten of unifom illumination to find the coespondence of pixels. Tajima and Iwakawa [14] suggest a ainbow patten, whee two images of the scene ae taken with diffeent colo filtes and the atio between the images eveals a unique code. This method can theoetically each high esolution. But pactically the extaction of the coect intensity value fo each location is difficult. The poposed imaging technique oiginally combines the stengths of timemultiplexing and spatial neighboing stuctued lighting pattens to fom dynamic pseudo-andom codes. 2 Dynamic Stuctued Light Imaging System The poposed imaging appoach uses pseudo-andom codes to ceate a pojected patten which is pogessively shifted hoizontally and vetically to incease the numbe of 3D econstucted points. As such, advantage is taken of the same uniquely coded featue points seveal times. A steeoscopic acquisition system is designed to acquie

3 Stuctued Light Steeoscopic Imaging with Dynamic Pseudo-andom Pattens 689 clouds of 3D points fom which coloed 3D suface models of objects ae computed. The chaacteistics of the senso and pojected patten ae defined hee. 2.1 System Desciption and Calibation The active steeo ig used fo expeimentation consists of two Lumenea LU135C colo CCD cameas with a 1392x1040 esolution, equipped with fixed focal 8.5 mm lenses, and linked togethe by a igid backet. An Electohome EPS1024 pojecto with a esolution of 1024x768 is also used, as in Fig. 1a. a) b) Fig. 1. a) Steeo stuctued lighting acquisition system, and b) calibation patten The use of a steeoscopic appoach athe than a classical stuctued light system with only one camea [10] eliminates the need fo calibation between the pojecto and the cameas. This solution gives access to focusing, zooming and bightness settings of the pojecto to adapt to vaious opeating conditions and depth without influencing the calibation of the acquisition system. The espective intinsic and extinsic paametes of the two cameas ae estimated successively using Zhang s calibation algoithm [15]. The calibation pocedue is facilitated by a custom calibation outine with audio cues that guides the use to popely position the checkeboad patten in the ovelapping field of the view of the cameas as shown in Fig. 1b. Ten images of the checkeboad patten ae equied to complete the calibation. In the cuent configuation, the senso s depth of view anges fom 693 mm to 3130 mm. The minimum depth is limited by the focusing capability of the pojecto, while the maximum is constained by the powe of the pojecto s lamp to ensue that featues ae accuately peceived by the cameas. At minimum and maximum depth, the senso espectively achieves a esolution of 0.61 mm and 1.53 mm. 2.2 Pseudo-andom Patten Definition and Pojection A bi-dimensional pseudo-andom (PR) patten defined as a gid of squae coloed egions is pojected on the suface of the object to be modeled. Colo patches ae selected instead of intesecting lines because they educe the ambiguity in the colo ecognition and can be pojected with high esolution using a LCD pojecto. Uniquely defined PR codes composed of a 3x3 neighbohood of squae colo egions ensue simplicity in the patten geneation and obustness in the image acquisition. They also povide enough infomation fo the integation of a minimum Hamming distance citeion between codes to ensue highe discimination.

4 690 P. Payeu and D. Desjadins The seies of 3x3 codes that compose the PR aay is geneated off-line fo a selected numbe of colos, k, following a pseudo-andom iteative pocess [16]. Squaes of a andom colo ae successively added to the aay while testing fo the uniqueness of the esulting 3x3 colo code thoughout the gid [17]. The esult is a gid of squae colo egions whee evey 3x3 sub-aay appeas at most once in the complete patten of NxM colo elements. Since the size of a colo code is 3x3, a nine-element vecto V (ij) defines evey local colo code such as: V = V, V, V, V, V, V, V, V, V } i = 1,... N, j = 1,... M (1) ( ij) { ( ij)1 ( ij)2 ( ij)3 ( ij)4 ( ij)5 ( ij)6 ( ij)7 ( ij)8 ( ij) 9 Evey V (ij) must also diffe by a defined minimum Hamming distance h in ode to be valid. The Hamming distance between two codes V (ij) and V (i j ) is defined as: 9 H ( ij, i' j') = ( δ ) whee: = 1 0 v( ij) = v( i' j') δ = (2) 1 v( ij) v( i' j') The PR patten composed of NxM elements is smalle than the pixel esolution of the pojecto such that the exta pixels available ae used to mak a delimitation of n black pixels aound evey colo egion. This makes colo code segmentation and ecovey moe obust when scanning scenes with vaying depth and shap inclinations. As coloed egions ae skewed accoding to the oientation and distance to the suface, vaious coloed egion sizes tend to appea in the images. Each colo element is pojected as a colo egion of PxQ pixels, sepaated by ows and columns of n pixels. Moeove, inceasing the numbe of colos, k, in the patten enlages the numbe of possible code pemutations. Also, the k colo components can be intechanged to povide adaptation to colos aleady pesent in the scene. Selected colos fom the colo palette should howeve emain fa apat in the hue space. Fo ou expeiments, P, Q and n, wee all set to 9 pixels. The PR patten contains 55x40 colo codes with a palette size of k=3 colos, namely Red, Geen, and Blue, as shown in Fig. 2a. The pojection of the patten is dynamic in that the colo gid is shifted hoizontally and vetically seveal times, esulting in the pojection of the same PR colo patten at seveal locations ove the scene. Steeoscopic images ae collected in synchonization fo evey location of the PR patten. As a esult, a lage numbe of unique featue points ae ceated. This leads to a dense 3D econstuction of the objects, in spite of a elatively low numbe of codes equied in the PR patten. a) b) c) Fig. 2. a) Pseudo-andom patten, b) its pojection ove an object, and c) dynamic pojection of the pseudo-andom patten to incease esolution

5 Stuctued Light Steeoscopic Imaging with Dynamic Pseudo-andom Pattens 691 The patten is successively shifted fom left to ight by x pixels fo u incements and fom top to bottom by y pixels fo v incements. These shifting paametes can be adjusted on demand to suit the desied esolution of the econstuction. The dynamic patten pojection is illustated in Fig. 2c, whee the X symbols epesent the successive line and column positions of the cente point (dak) of a given coloed egion (gay aea). Because the colo egions contain 9x9 pixels and ae sepaated by black bodes of n=9 pixels, a maximum of u=18 hoizontal and v=18 vetical positions of the patten, shifted by x= y=1 pixel each, ae possible. This povides a maximum incease of the esolution by 324 times what is pemitted with the initial 55x40 aay of codes. Oveall, a maximum of uniquely matching featues can be ceated and thei position estimated in 3D space. In compaison with solely time-multiplexing solutions, the poposed dynamic PR codes do not intinsically equie multiple pojections to estimate the shape of an object. A single patten pojection ensues a fai distibution of featues points ove the entie pojection suface. The dynamic patten appoach augments the esolution on demand, and only if equied. It epesents an oiginal combination of spatial neighboing and time-multiplexing stuctued light techniques. Accuacy can be set at vaiable levels by selecting the desied numbe of hoizontal and vetical shifts of the dynamic patten, depending on the acquisition and computation time available. 3 Image Pocessing and Code Recovey The calibated steeoscopic pai of cameas collects images of the scene with vitual featues fo evey position of the PR patten. The unique colo codes extacted fom the patten ensue a eliable match between the left and ight views. This leads to accuate 3D point estimation. The image pocessing pefomed on evey pai of images and the validation of colo codes ae descibed in this section. 3.1 Colo Regions Extaction and Gouping The extaction of the PR codes fom one pai of colo images is pefomed in a seies of steps. Fist, colo egions ae segmented fom the black subdivision aeas in the HSV colo space. An intensity histogam is computed on the hue dimension to educe the numbe of paametes equied to popely segment an image. The peaks of the histogam detemine the dominant colos in the image and isolate the coesponding egions that ae mapped onto k sepaate binay sub-images. An evaluation of the peaks and valleys of the histogam with adaptive theshold is pefomed to disciminate the k dominant chomatic components. This appoach eveals to be obust to lage vaiations in the mapping of colos in the images. Next, goup labeling is pefomed on evey sub-image to cluste consistent egions based on thei geometical poximity. Howeve, given that the pojected patten tends to ceate colo blobs with non-unifom intensity distibution, some colo egions adopt vaiable shades of a colo, and can theefoe be incoectly segmented, as shown in Fig. 3a.

6 692 P. Payeu and D. Desjadins a) b) c) Fig. 3. a) Segmentation and code ecovey ove non-unifom colo distibution, b) egions with holes, and c) combined colo egions To ovecome this situation and ensue eliable colo code extaction, the thid step pefoms a statistical goup analysis ove the dimension of each labeled egion, and eithe dops small blobs o tags goups that ae lage than the aveage egion fo futhe subdivision. The topology of extacted egions is fist analyzed to detect holes appeaing within a coloed egion, as shown in Fig. 3b. Such empty egions ae filled with the same colo as thei bounding pixels. The aveage size and standad deviation of the pixel count in evey labeled egion is calculated and a theshold is applied on the size of the egions. The egions that ae lage than the aveage size plus one standad deviation ae futhe segmented in the next step. The egions that ae unde 5 pixels ae consideed noise and ae discaded. The fouth step splits lage egions into smalle goups to eliminate meged colo egions. The latte can appea in the images due to the skew ceated by vaiable suface oientations, as shown in Fig. 3c. Such situations ae detected fom the assumption that all colo egions should be appoximately of the same size ove a local neighbohood in the image. Any egions that ae significantly lage ae futhe segmented. The aveage intensity, V channel of the HSV colo space, and its standad deviation ae calculated ove the entie aea of the meged colo egions. The addition of the aveage intensity and its standad deviation is used as a theshold that is locally applied to eliminate lowe intensity pixels. Such pixels being typically found on the bodes of colo egions, this pocedue povides the desied disconnected egions. Finally, a second goup labeling opeation is pefomed on the thesholded images and the detected colo egions ae added to a goup list. The esult is k lists of goups, one fo evey dominant colo found, which ae then used to ecove the PR codes. 3.2 Pseudo-andom Code Validation Pseudo-andom codes ae ecoveed by tavesing the list of colo egions thoughout the k colo image masks. The nine closest geometical egions to a given colo egion ae detemined by calculating the squae distance between the cuent colo egion and all emaining colo egions in the espective colo masks. Given that a code is composed of 3x3 neighboing colo egions, the nine colo egions with the smallest squae distance ae consideed as being pat of a code. Afte being identified based on thei geometical poximity to a given colo egion, the nine closest colo blobs ae soted vetically and hoizontally to detemine thei coect spatial location.

7 Stuctued Light Steeoscopic Imaging with Dynamic Pseudo-andom Pattens 693 Next, code validation is pefomed by computing a confidence map on the etieved colo codes. Evey element in a 3x3 colo code, besides the middle element, is pat of othe 3x3 codes. This means that a given code will also have eight neighboing codes containing at least one of its elements, except on the contous of the PR patten. These neighboing codes ae extacted fom the captued image and compaed to the known oiginal pojected patten. The numbe of neighboing codes that can be locally matched between the image and the oiginal patten defines the confidence, S(x), attibuted to the given code, C(x), which is defined as: S( x) x+ 1 i= x 1 x+ 1 = j image [ C ( i, j) C ( i, j)] = x 1 oiginal (3) whee ^ epesents the intesection logical opeato. Codes that ae not pesent in the oiginal pojected patten ae discaded. Any duplication of codes is detected and the confidence level of such codes is analyzed. The codes with the highest confidence level ae kept and the othes ae discaded. 4 3D Reconstuction and Textue Mapping Fom the list of PR codes ecoveed that pass the confidence level citeion, evey unique PR code found in the left and ight images epesents a eliable matching featue point atificially ceated ove the scene. The final opeation consists of computing the location of the coesponding physical points in 3D space and mapping thei colo and textue to povide an accuate model that can be eadily intepeted. In ode to eliminate outlies and poduce smoothe 3D models, a andom sample consensus (RANSAC) algoithm is applied on the esulting list of points coesponding to the ecoveed and validated colo codes. The optimal polynomial econstuction algoithm of Hatley et al. [18] which uses a tiangulation method with a minimizing citeion based on the epipola geomety is applied to econstuct the 3D cloud of points coesponding to evey matched colo coded featue point. This final phase poduces a list of 3D points that model the suface shape. To ensue that the colo and textue infomation mapped on the model is epesentative of the actual chaacteistics on the suface of the object, povision is taken to collect an image of the scene without any patten being pojected pio to the 3D acquisition. As the senso is kept at a constant position and oientation thoughout the entie acquisition phase, the colo images collected a pioi offe a pixel-wise coespondence with the seies of images collected while the pseudo-andom patten is dynamically shifted ove the scene. Fo evey 3D econstucted point, the coesponding pai of 2D pixel matches is queied fo thei associated RGB colo infomation. The colo paametes associated to a given 3D point is detemined by aveaging the coesponding pixels colo fom the left and the ight images. In the pesent wok, an OpenGL viewe is developed that intepolates the colo infomation between the defined vetices and fills the colo ove missing egions by intepolation. As a esult, accuate colo infomation is associated with evey suface patch displayed along with the shape of the objects. This enables a visually consistent model, both in shape and textue, when compaed to the oiginal chaacteistics of the scene.

8 694 P. Payeu and D. Desjadins 5 Expeimental Evaluation Expeimentation with the poposed dynamic stuctued light steeoscopic senso was conducted on seveal types of objects with diffeent eflectance popeties, suface complexity, colo distibution and intensities. Due to space limitations, only a subset of 3D models is pesented to demonstate the quality of coloed econstuction. The fist object consideed is a caft basket whose suface exhibits small vaiations in depth and contains a combination of matt and highly eflective aeas, as shown in Fig. 4. This object was scanned at maximum esolution with 324 shifted pattens. a) b) c) d) Fig. 4. 3D modeling of a basket with vaiable eflectance popeties: a) colo image of the basket, b) PR patten pojection, c) suface mesh econstuction of the font face, and d) textued 3D model The econstuction is accuate consideing the complexity of the textues pesent on the object. The density of econstucted 3D points emains lowe ove the diagonal golden stipes that exhibit high eflectivity. But the successive pojections of the dynamic PR patten collect sufficient infomation ove those aeas. A high density of points is achieved ove the non-eflective stands of the basket in spite of the nonunifomity and oughness of the suface. The colo mapping ove the 3D suface mesh is accuate and clealy endes the infomation about all egions of the basket s face. The second example pesented in Fig. 5a is a heavily textued chai back est that exhibits non unifom colos and vaying degees of cuvatue ove its suface. The back est was scanned with only 25 shifts of the PR patten peviously defined. Even though only 8% of the possible pojections wee used, the econstuction of the back est, which contains points, is vey accuate. The cuvatues of the object clealy appea in Fig. 5b whee the font and lateal views of the suface mesh ae pesented. The patches of dak colo pesent on the chai do not influence the econstuction pocess and the model shows unifom density in spite of the stong vaiation in textue on the object. When colos ae mapped on the suface mesh, the subtle details of the textues ae cisp and clealy visible, as shown in Fig. 5c. The compaison between the coloed econstuctions on these two objects with the oiginal images demonstates that the poposed 3D imaging appoach can povide dense econstuction of suface shapes and yield coloed epesentations that ae vey close to the oiginal textue of the object, in spite of thei inheent complexity. In tems of pefomance, the steeoscopic imaging system takes on aveage 13.9 seconds to pefom the acquisition, image pocessing, code ecovey, 3D econstuction and textue mapping steps fo one pojection of the PR patten. This epesents less than

9 Stuctued Light Steeoscopic Imaging with Dynamic Pseudo-andom Pattens 695 a) b) c) Fig. 5. 3D modeling of a chai back est exhibiting heavy and non-unifom colo pattens: a) colo image of the back est, b) suface mesh econstuction fom 2 pespectives, and c) textued 3D model fom 2 pespectives 10ms pe econstucted 3D point, which is acceptable fo a wide ange of applications. The possibility to adjust the numbe of shifted pattens on demand also offes a maximum of flexibility on the desied esolution and pocessing time. 6 Conclusion This pape pesents an oiginal stuctued light steeoscopic imaging technique that uns entiely fom affodable off-the-shelf equipment, and combines spatial neighboing and time-multiplexing stuctued light appoaches to build 3D coloed model of objects with an adaptive esolution. The espective stengths of both stuctued lighting techniques ae combined in an efficient and integated way. The concept of dynamic pojection of pseudo-andom pattens is intoduced to povide the necessay flexibility to achieve dense 3D model when equied with no modification to the pojected patten and to the acquisition system. A obust appoach to ecove coloed pseudo-andom codes is pesented that builds upon statistical code validation to minimize the occuence of mismatches while emaining independent fom the configuation of the objects, thei eflectance popeties and textues. The esulting models ae full coloed econstuctions of the objects that enable accuate epesentations and make intepetation easie. Expeimental esults on objects with complex suface popeties demonstate the quality and eliability of the models obtained with the poposed 3D imaging technique. Refeences 1. Maszalec, J., Myllylä, R.: Shape Measuements Using Time-Of-Flight-Based Imaging Lida. In: SPIE Confeence on Thee-Dimensional Imaging and Lase-based Systems fo Metology and Inspection III, vol. 3204, pp SPIE Pess, Pittsbugh (1997) 2. Blais, F., Taylo, J., Counoye, L., Picad, M., Bogeat, L., Godin, G., Bealdin, J.-A., Rioux, M., Lahanie, C.: Ulta High-Resolution 3D Lase Colo Imaging of Paintings: the Mona Lisa by Leonado da Vinci. In: Castillego, et al. (eds.) Lases in the Consevation of Atwoks, pp CRC Pess Taylo & Fancis Goup, London (2008) 3. Salvi, J., Pagès, J., Batlle, J.: Patten Codification Stategies in Stuctued Light Systems. Patten Recognition 37(4), (2004)

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