Incremental Reconstruction Approach for Telepresence or AR Applications

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1 Incremental Reconstructon Approach for Telepresence or AR Applcatons Lus Almeda ISR, Unv. Combra Polytechnc of Tomar Tomar, Portugal Paulo Menezes ISR, Unv. Combra Combra Portugal Jorge Das ISR, Unv. Combra Combra Portugal Abstract Ths paper proposes an on-lne ncremental 3D reconstructon framework amed at fulfllng the needs of telepresence or human machne nteracton applcatons. The research presents a teleconference system that mproves and nduces the feelng that persons are n the presence of each other. A free vewpont method, based on realstc user s appearances, s proposed to smulate a real face-to-face meetng. The contrbutons are: a new ncremental verson of Crust algorthm that enables ncremental fuson of sensor data and a confdence-based method that automatcally decdes whether or not to ntegrate newly acqured data n the exstng model based on measure uncertanty and novelty. To avod the classcal stereo vson reconstructon problems, the method bases on hybrd sensors to acqure smultaneous depth nformaton and the correspondng texture mage (e.g. knect). Ths enables the algnment between acqured data and pre-adqured model by maxmzng a crteron that s related wth the matchng between vsual features and between acqured shapes. A mesh based representaton enables the use of the surface topologcal geometrc nformaton durng the data model ntegraton process. Keywords Three-Dmensonal Graphcs and Realsm, Augmented Realty, Reconstructon, Range Data, Trackng, Telepresence 1 INTRODUCTION Wdely used vdeo teleconference applcatons (ex: Csco WebEx, Ctrx GoToMeetng, Mcrosoft Skype, Google Hangouts or Apple Facetme) are not replcatng mportant real face-to-face meetng cues, lke eye-to-eye contact establshment, gesture reconnassance, body language or facal expressons. Nevertheless, recent advances on sensng, dsplay and computaton technology are creatng the deal condton for affordable consumer 3D applcatons n Augmented Realty (AR), Vrtual Realty (VR) or Human Machne Interactons (HMI). Our applcaton concept goal s depcted n Fgure 1, where user s locatons setup, deally equpped wth dsplays, vdeo cameras, depth sensor, mcrophones and speakers, enables users to communcate and nteract remotely experencng the benefts of a faceto-face meetng n full sze. It ncludes a 3D capture, reconstructon and vrtual vew synthesys dsplay system. There are some notable works that realstcally explot the user s appearance for tele-mmerson lke those developed at UC Berkeley [Kurllo 08] and at GrImage at INRIA [Pett 09]. Both use vdeo cameras array to perform real-tme full body 3D reconstructons leadng to some weaknesses, lke: reconstructon problems due to the lack of accuracy n low-texture or repeated pattern re- 3D Scene User A Locaton Multvew 3D data capture Renderng Transmsson Regstraton Head/eye gaze tracker Dsplay 3D Model Representaton Observer User B Locaton Fgure 1. Face to face meetng through technology medaton, lne of sght preservng method. Overvew of the reconstructon algorthm that ams to contnuously generate a realstc body model, transfer the model and reconstruct on a remote common dsplay or vrtual envronment accordng, each user s vewpont by a trackng process. The proposed real-tme 3D full reconstructon system combnes vsual features and shape-based algnment between consecutve pont clouds whle the mesh model representaton s updated ncrementally usng a new Crust based algorthm.

2 gons, hgh cost acquston data setups, hgh power computatonal requrements, and ther unsutablty for domestc use. Recent RGB-D reconstructon related works are usng algnment and ntegraton approaches based on SLAM sparse methods [Beck 13][Almeda 13]. Henry et al. [Henry 12] combne vsual feature matchng wth ICPbased pose estmaton to buld a pose-graph whch they optmze to create a globally consstent map. Newcombe et al. [Newcombe 11] presented an mproved accurate soluton known as KnectFuson whch uses a new algorthm for real-tme dense 3D mappng. KnectFuson ntegrates depth maps from the Knect nto a truncated sgned dstance functon (TSDF) representaton. The requred algnment to fuse the depth maps s based on the teratve closest pont algorthm (ICP), that runs on a GPU for obtanng real tme performance. Our contrbuton s a real-tme 3D full reconstructon system that combnes vsual features and shape-based algnment between consecutve pont clouds whle the mesh model representaton s updated ncrementally usng a new Crust based algorthm. The paper s organzed as follows. Secton 2 descrbes the proposed reconstructon methodology, secton 3 presents some expermental results and dscusson and, secton 4 presents the future work and conclusons. 2 MESH GENERATION Fgure 2. Mesh model usng Crust trangulaton An ncremental adaptaton of Crust algorthm s proposed and enables the addton of new 3D ponts wthout havng to recompute prevous generated meshes. The sttchng process reles on ntegratng new mesh poles as new vertces, on trangulaton step and compute trangles only where both surfaces share vertces. Gven a set of regstered ponts X R 3 sampled from an object surface S, t s possble to approxmate ts shape by a trangle mesh. The approach, based on a modfed Crust algorthm [Amenta 98], uses a set of ponts P from the medal axs (polos) to extract a subset from the Delaunay trangulaton of X that approxmate S. The polo ponts, obtaned from the Vorono vertex or trangles average outer normal s, are postve (p + ) f they le on the convex sde of the surface and negatve (p ) otherwse. Once computed the Delaunay trangulaton of X P, the surface mesh s estmated by extractng the set of smplces whose vertces belong to X. The proposed approach adds an ncremental characterstc to the Crust algorthm as t s effcent vable to add new vertces to a Delaunay trangulaton. Assumng that a set of ponts X t were already processed by the Crust algorthm, the set of poles P t and the Delaunay trangulaton are also avalable [Almeda 11]. To add a new set of sample ponts X t+1 to the surface mesh, avodng a complete mesh recalculaton, the followng steps are performed: Algorthm 1 Crust ncremental algorthm 1: P t+1 =poles of X t+1 2: Add P t+1 X t+1 as new Delaunay trangulaton vertces 3: Extract trangles whose vertces belong to X t X t+1 The procedure can be appled repeatedly to accommodate any number of pont sets X. Nevertheless to avod progressve grow n the number of mesh vertces, ponts closest to the mesh vertex (.e. under a gven Eucldean dstance threshold) are deleted from the nput pont cloud before the ncremental Crust step. Fgure 2 llustrates a mesh model usng the Crust approach. Multvew 3D Scan: Recent depth sensor devces, lke XBOX Knect provde 3D measurements and also RGB data, enablng the use of 2D mage algorthms. It s possble to mprove the 2D feature mappng between consecutve RGB mages, assocatng the respectve depth data and creatng a 3D feature trackng. The Xbox 360 R Knect TM Sensor combnes a RGB camera and a structured lght 3D scanner, consstng of an nfrared camera and an nfrared (IR) laser projector. The depth measurement prncple s based on a trangulaton process [Freedman 10]. Regstraton: The regstraton process enables to algn several 3D pont clouds nto one same referental to create a global model (Fgure 3(b)). To regster new 3D pont clouds, acqured from dfferent pont of vews, we perform algorthm 2 steps (Fgure 3(a)): Algorthm 2 Regstraton algorthm 1: Select one 3D pont cloud shape to be the approxmate 3D mean shape (ex: scan 0). 2: Algn the 3D pont cloud shapes: - Compute the centrod of each 3D pont cloud shape (or set of nvarant features). - Algn all shapes centrod to the orgn. - Normalze each shapes centrod sze. - Compute the rgd-body transformaton usng expresson (5) to (7) to obtan the rotaton R and translaton t whch best algns both 3D shapes. 3: Apply the calculated transformaton to obtan new approxmate 3D mean shape Consderer the exstence of two correspondng 3D ponts

3 Scannng k Scannng k-1 SURF Sparse Features RGB Dense Pont Clouds Depth (1) center of mass translaton Integraton Trangulaton Mappng Mesh Update R,T Confdence evaluaton Smplfcaton Color ntegraton 3D Model Regstraton Overlap Removng (2) algn shapes centrod to the orgn and compute rgd-body transformaton. (a) (3) apply the calculated translaton and rotaton Range uncertanty 3D Model (dstance, object pose) (b) Fgure 3. (a) Regstraton smplfed flow. (b) Algorthm overvew modules sets {x t } and {xt+1 }, = 1..N, from consecutve t and t +1 scans, whch relatonshp s gven by equaton (1): x t+1 = Rx t + t + v (1) ε 2 = N =1 x t+1 Rx t t 2 R represents a standard 3x3 rotaton matrx, t stands for a 3D translaton vector, and v s a nose vector. The optmal transformaton R and t that maps the set {x t } on to {xt+1 } can be obtaned through the mnmzaton of the equaton (2) usng a least square crteron. The least square soluton s the optmal transformaton only f a correct correspondence between 3D pont sets s guaranteed. Complementary methods are used to robust the correspondence (e.g. RANSAC). The sngular value decomposton (SVD) of a matrx can be used to mnmze Eq. (2) and obtan the rotaton (standard orthonormal 3x3 matrx) and the translaton (3D vector) [Arun 87][Challs 95][Eggert 97]. In order to calculate rotaton frst, the least square soluton requres that {x t } and {xt+1 } pont sets share a common centrod. Wth ths constrant a new of equaton can be wrtten usng the followng defntons: (2) defned by H = x t+1 c (x t c )T. Consderng that the sngular value decomposton of H results on H=UDV T, then the optmal rotaton matrx, R, that maxmzes the referred trace s R= U dag(1; 1; det(uv T )) V T : R = UV T (6) The best translaton that algns {x t+1 } centrod wth the rotated {x t } centrod s Model Mappng t = x t+1 Rx t (7) Suppose that the mappng from the world coordnates to one of the scans of the sequence, s known (ex: scan 0) and t s represented by the transformaton 0 T w. As descrbed before, for any consecutve par of scans (t, t+1) from tracked ponts t s possble to estmate rotaton and translaton and combne[ them] nto a sngle homogeneous R t matrx 4x4, t+1 T t, T =. 0 1 Therefore t s possble to compute equaton ( 8): x t = 1 N N =0 x t x t+1 = 1 N N =0 x t+1 (3) x t c = xt xt x t+1 c = x t+1 x t+1 (4) ε 2 = N =1 x t+1 Rx t c 2 (5) c Maxmzng Trace(R H) enable us to mnmze the generated equaton (5), wth H beng a 3x3 correlaton matrx T 0 = T 1 1 T T 0 T w = T 0 0 T w (8) To update the reconstructed model, each acqured 3D pont set s transformed to the world coordnate system usng T w. Ths algnment step adds a new scan to the dense 3D model. Algnment between successve frames enables to track the body poston over small dsplacements. Correspondence: the descrbed 3D regstraton method requres the knowledge of pont correspondences between the exstng 3D ponts set and the newly acqured pont

4 set. To solve ths correspondence problem, we take advantage of the fact that RGB-D sensor provdes smultaneously scene 3D nformaton and respectve 2D mage. We propose the use of Robust Image Features (lke Bay s Speeded Up Robust Features (SURF) [Bay 06]), whch enables the dentfcaton of one same pont n consecutve mages. The assocaton of a vsual feature wth ts 3D pont, enables to establsh a match between consecutve 3D pont clouds. Although the SURF features enable the establshment of correspondences between ponts from both sets, llumnaton and vewponts changes, together wth sensor nose, among others, nduce varatons on those extracted features that may contrbute to errors n the parng process. Ths may ndeed destroy the transformaton estmaton process by ntroducng unacceptable error or leadng to no solutons. For ths reason we use the RANSAC algorthm [Fschler 81] to remove false correspondent pont pars that wrongly bases the rgd body transformaton estmaton. The approach randomly samples three 3D ponts correspondent pars from consecutve scans and teratvely estmates the rgd body transformaton [Arun 87] untl fnd enough consensus or reach a maxmum number of teraton based on the probablty of outlers. The regstraton method wth outlers removal s descrbed n followng algorthm 3. Integraton: A new 3D mesh acqured a from dfferent pont of vew and regstered nto a 3D global model can lead to two stuatons: (1) some non-overlapped trangles contans new nformaton for the 3D model and (2) some overlapped trangles mght contan redundant data, or more confdent data useful for the model refnng. To choose whch nformaton s relevant, we evaluate the data based on the uncertanty of range sensor. Sensor accuracy measures are dependent on the ncdent angle between the measurng ray and the surface dstance. Overlappng segmentaton, front face checkng and matchng: the overlappng regon s determned by projectng the pre-bult mesh vertces s nto the sensor 2D plane, once transformed for the referental of the newly scanned vertces and by checked the ntersecton area. We could smply re-trangulate all the ponts on the overlappng regon, but due msalgnment errors t can result on a bumpy surface. To tackle ths challenge we propose an approach, where the trangulatons update only happens f t contrbutes to mprove the global model. The process consst n detectng overlappng trangles on the prevous scanned range data mage and the newly scanned range, and then keep those that provde more nformaton for the model. We assocate to each trangle a confdence value based on the measure uncertanty of ts 3D vertces. The dstance from where sensor acqures the data and the angle from t stands n front a surface are nversely proportonal to the confdence (eq. 9): Algorthm 3 Regstraton algorthm wth outlers removal 1: Input :X p,x q {assumed correspondent 3D pont pars} 2: Output :[R, t] {rgd body transformaton estmaton} 3: whle ( < MAXIT ER) do 4: randomly select 3 pars of ponts 5: [R,t ] estmate 6DOF rgd body transformaton for these 3 pars 6: X q = R X q +t {apply the transformaton to X q scan to map t nto X p reference frame} 7: nlers = (X q X p ) < τ,number o f nlers {determne the set of data ponts whch are wthn a Eucldean dstance threshold τ} 8: f (szeo f (nlers ) > T threshold ) then 9: [R,t] re-estmate the transformaton model usng all nlers 10: EXIT 11: end f 12: f (number o f nlers > bestscore) then 13: bestscore number o f nlers 14: best nlers nlers {store cardnalty of nlers and nlers } 15: update MAXIT ER 16: end f 17: = : end whle 19: [R, t] re-estmate the transformaton model usng all ponts from best nlers

5 C = 1 Lθ (9) where L s the dstance between a 3D pont and the range sensor s optcal center and θ represents the sensor s pose angle n relaton to the surface. The angle θ s gven by equaton (10) θ = arcos( n, r ) (10) where n s the normal of a trangle and r s the normalzed measurement ray from the sensor s optcal center to the pont. The confdence measures capture the fact that ponts close to the sensor, as surfaces close to a fronto-parallel orentaton, are typcally captured more accurately by range sensors. The normal vector of a pont conssts of averagng normal vector of trangles formed wth pars of neghbors, and for each new scanned 3D mesh, a lst of trangles (3D faces) s be tagged wth confdence nformaton related wth ts 3D pont postons. Integraton of new trangles wll occur, only f, ts confdence contrbutes to mprove the 3D model. Fgure 4 depcts the prncple of a range sensor, composed by 3 ray measure beams, scannng an object from dfferent postons (2D example). In ths case, the range sensor acqures data from 4 dfferent pont of vews, S 0,S 1,S 2,S 3. For example, due overlappng data measures, between S 0,S 1,S 3 we can ncrementally update the global model wth the more confdent edges (ex: P 30,P 31,P 32 ). Flterng Methods: depth maps contanng holes, nconsstent data n the depth mage object boundares and vbratng behavor at the depth pxel level should be addressed to mprove 3D reconstructons. Temporal flterng methods based on tme data averagng clearly mproves the depth maps qualty, although are mpractcal on real-tme applcatons or where movng objects exst. Several nose removal methods are possble to enhance the Knect depth maps qualty [Tomas 98][Pars 06], lke medan flter, blateral flter, jont blateral flter, non-local means flter or movng square fttng. For example, the blateral flter s a non-lnear flter based on Gaussan dstrbuton, whch reduces the nose smoothng the sgnal whle preservng the edges, however t has a hgh computatonal cost. 3 RESULTS The ntegraton and mesh refnng algorthm were prevously tested n matlab wth nose free pont data set and provded useful hnts to understand the system. Fgure 5 depcts a 3D mesh model of an object (lght blue) for whch the face trangles normals were computed (red arrows). These trangles and vertces s are projected nto the RGB-D sensor plane, here represented by the lght green square. The coordnate referental s composed by the blue axes and ts ntersecton s the projecton center (referental orgn). The face trangles projectons are represented n yellow. In Fgure 6, the object s rotated slghtly around ts axes, here represented by lght green color. Knowng the rgd transformaton, the vsble vertces are transformed to match wth the prevous model and reprojected nto the sensor plane, Fgure 7. The re-projecton of the mesh nto mage sensor plane enables to detect the trangle ntersecton and preserve trangles wth hgher confdence. P00 P30 P10 P01 P31 Object n j n j P32 P02 P11 P20 P12 n j S3 P21 P22 S - range sensor at poston S0 S1 S2 Fgure 4. Range sensor, composed by 3 ray measure beams, scans an object from dfferent postons (2D example) Fgure 5. Fxed range sensor scannng an object In Fgure 9 we show an example of correspondence between consecutve mage features usng SURF method (whte lnes ndcate correspondent pont). Fgure 10 depcts a sequence of scans that creates a 3D per-

6 Fgure 10. Sequence of mesh models to be ntegrated, trangulaton based on depth data sensor grd structure and depth nformaton. Fgure 7. Mesh re-projecton nto mage sensor plane to detect trangle ntersecton. Preserve trangles wth hgher confdence. Fgure 6. Movng object (a) (b) (c) Fgure 8. (a) RGB mage. (b) IR monochromatc mage wth speckles pattern projected onto a scene. (c) Depth map wth dstances assocated to colors.

7 Fgure 9. SURF features matched on consecutve tme frames son model. On the top row we present RGB mages of the scene and n lower row snapshots of the respectve meshes, generated n real tme. The mesh trangulaton s based on depth data sensor grd structure and depth nformaton. To acheve the real tme characterstc, we programmed n OpenGL for Embedded Systems (OpenGL ES) as t enables vertex buffers to be processed n parallel as a sngle entty. GPU shaders and OpenCV [OpenCV 15] were also used. Fgure 11 shows a reconstructed 3D model. It results from several 3D pont clouds fused n real tme after applyng successve 3D rgd body transformatons, mesh refnng ntegraton and renderng. ponts can generate erroneous matches due mage nose and they are more common on body boundares (Fgure 9 presents some wrong dagonal lnks for an almost pure vertcal axs body rotaton). The body to be reconstructed should be segmented from background statc areas usng a moton flter. Scale-nvarant feature transform (SIFT) [Lowe 04] was also tested and presented better accuracy as key feature descrptor, although we have chosen SURF method n order to acheve the real-tme characterstc. The knect system magng geometry ntroduces structural errors that are functon of the dstance to the object and the sensor orentatons relatve to the object surface. A proper calbraton of the RGB-D sensor s essental to mprove results. Stereo calbraton procedures were used to estmate the ntrnsc parameters of both RGB and IR (depth) cameras, as the relatve transformaton (R,T ) between them. The estmated camera s parameters and transformatons TM enabled us to algn Knect both RGB wth IR (depth) cameras and obtan more relable data nformaton (as depcted n Fgure 8). The proposed reconstructed 3D model approach enables to generate any vrtual syntheszed vew for an observer that moves n front of a dsplay, that s, a requred augmented realty (AR) functonalty. 4 CONCLUSION A free vewpont system framework s proposed to generate vew dependent synthess based on scene 3D mesh model. Our approach explores vrtual vew synthess through moton body estmaton and hybrd sensors composed by vdeo cameras and a low cost depth camera based on structured-lght. The soluton addresses the geometry reconstructon challenge from tradtonal vdeo cameras array, that s, the lack of accuracy n low-texture or repeated pattern regon. We present a full 3D body reconstructon system that combnes vsual features and shapebased algnment. Modelng s based on meshes computed from dense depth maps n order lower the data to be processed and create a 3D mesh representaton that s ndependent of vew-pont. Research contrbutons nclude a new ncremental verson of Crust algorthm that effcently adds new vertces to an already exstng surface wthout havng to recompute prevous generated meshes and a topologcal ncremental reconstructon approach based on confdence measures that avods redundant data nformaton computaton. Fgure 11. Syntheszed vews of a on-lne 3D reconstructed model dependent of observer pont of vew. 3.1 Dscusson Processng real data allowed us to dentfy some nose sources that can affect the algorthm. For example, SURF Wth ths on-lne reconstructed 3D model, we can provde synchronous pont of vew for an observer that moves n front of a dsplay of a face-to-face meetng applcaton, thus enhancng the presence sensaton. Future work ncludes framework usablty tests for a telepresence meetng applcaton. Ths work presents an on-lne ncremental 3D reconstructon framework that can be used on low cost telepresence applcatons, augmented realty (AR) or human robot nteracton applcatons. References [Almeda 11] Lus Almeda, Flpe Vasconcelos, Joa o Barreto, Paulo Menezes, and Jorge Das.

8 [Almeda 13] [Amenta 98] On-lne ncremental 3d human body reconstructon for hm or ar applcatons. In CLAWAR 2011: 14th Internatonal Conference on Clmbng and Walkng Robots and the Support Technologes for Moble Machne. Pars, France, September Lus Almeda, Paulo Menezes, and Jorge Das. Handbook of Research on ICTs for Human-Centered Healthcare and Socal Care Servces, chapter Augmented Realty Framework for the Socalzaton between Elderly People, pages IGI Global, Nna Amenta, Marshall Bern, and Manols Kamvyssels. A new vorono-based surface reconstructon algorthm. In Proceedngs of the 25th annual conference on Computer graphcs and nteractve technques, SIGGRAPH 98, pages ACM, New York, NY, USA, [Arun 87] K. S. Arun, T. S. Huang, and S. D. Blosten. Least-squares fttng of two 3-d pont sets. IEEE Trans. Pattern Anal. Mach. Intell., 9: , September [Bay 06] Herbert Bay, Tnne Tuytelaars, and Luc Van Gool. Surf: Speeded up robust features. In In ECCV, pages [Beck 13] S. Beck, A. Kunert, A. Kulk, and B. Froehlch. Immersve group-to-group telepresence. IEEE Transactons on Vsualzaton and Computer Graphcs, 19(4): , [Challs 95] J. Challs. A procedure for determnng rgd body transformaton parameters. Journal of Bomechancs, 28(6): , jun [Eggert 97] D. W. Eggert, A. Lorusso, and R. B. Fsher. Estmatng 3D rgd body transformatons: a comparson of four major algorthms. MAchne Vson and Applcatons, 9: , [Fschler 81] Martn A. Fschler and Robert C. Bolles. Random sample consensus: a paradgm for model fttng wth applcatons to mage analyss and automated cartography. Commun. ACM, 24: , June [Freedman 10] Barak Freedman, Alexander Shpunt, Mer Machlne, and Yoel Arel. Depth mappng usng projected patterns, May [Henry 12] Peter Henry, Mchael Krann, Evan Herbst, Xaofeng Ren, and Deter Fox. Rgb-d mappng: Usng knect-style depth cameras for dense 3d modelng of ndoor envronments. I. J. Robotc Res., 31(5): , [Kurllo 08] [Lowe 04] G. Kurllo, R. Vasudevan, E. Lobaton, and R. Bajcsy. A framework for collaboratve real-tme 3d telemmerson n a geographcally dstrbuted envronment. In Multmeda, ISM Tenth IEEE Internatonal Symposum on, pages dec Davd G. Lowe. Dstnctve mage features from scale-nvarant keyponts. Int. J. Comput. Vson, 60:91 110, November [Newcombe 11] Rchard A. Newcombe, Shahram Izad, Otmar Hllges, Davd Molyneaux, Davd Km, Andrew J. Davson, Pushmeet Kohl, Jame Shotton, Steve Hodges, and Andrew Ftzgbbon. Knectfuson: Real-tme dense surface mappng and trackng. In Proceedngs of the th IEEE Internatonal Symposum on Mxed and Augmented Realty, ISMAR 11, pages , Washngton, DC, USA, IEEE Computer Socety. [OpenCV 15] OpenCV [Pars 06] Sylvan Pars and Frédo Durand. A fast approxmaton of the blateral flter usng a sgnal processng approach. In Proceedngs of the 9th European Conference on Computer Vson - Volume Part IV, ECCV 06, pages , Berln, Hedelberg, Sprnger-Verlag. [Pett 09] Benjamn Pett, Jean-Dens Lesage, Clément Mener, Jéréme Allard, Jean-Sébasten Franco, Bruno Raffn, Edmond Boyer, and Franços Faure. Multcamera real-tme 3d modelng for telepresence and remote collaboraton. INTERNATIONAL JOURNAL OF DIG- ITAL MULTIMEDIA BROADCASTING, 2010: , [Tomas 98] C. Tomas and R. Manduch. Blateral flterng for gray and color mages. In Computer Vson, Sxth Internatonal Conference on, pages , Jan 1998.

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