A Slit Scanning Depth of Route Panorama from Stationary Blur

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1 A Sli Scanning Deph of Roue Panorama from Saionary Blur Min Shi Jiang Yu Zheng Deparmen of Compuer Science Indiana Universiy Purdue Universiy Indianapolis Absrac This work achieves an efficien acquisiion of scenes and heir dephs along srees. During he movemen of a vehicle, a sli in he camera frame is se properly o sample scenes coninuously for a roue panorama. This paper proposes a novel mehod of deph esimaion by analyzing a new phenomenon named saionary blur in he roue panorama. We find is relaion wih he deph and evaluae is degree a local and global levels. The deph esimaion hrough filering avoids feaure maching and racking ha are error-prone in he scanning of real and comple sree scenes. Our mehod provides reliable resuls bu requires much less daa han ha of he srucure from moion. This keeps he elegance of he roue panorama in daa represenaion, and is suiable for real ime sensor developmen. Uilizing he compleeness of he roue panorama in he scene archiving, we can generae planar models of srees, which will be used in ciy visualizaion. 1. Inroducion Recenly, here are increasing demands for fas consrucion of urban models for ciy visualizaion. Alhough air-borne laser sysems have produced elevaion maps, he rendered images viewed from he ground have inadequae resoluion, even wih he fines LIDAR daa. On he conrary, he roue panorama (RP) has been proposed for archiving scenes along srees [5,6,7,1]. Coninuous views are obained from he sli scanning as a camera mouned on a vehicle moves along srees. The small daa size and seamless scene coverage make he roue panorama eendable o a long disance, which is a pracical soluion of virual ciy indeing and navigaion via nework [5]. The drawback of he roue panorama is he deformaion of he D parallel-perspecive projecion. This can be overcome by he deph eracion and 3D model rendering. Targeing a large urban area is more challenging han working in an indoor environmen. Because of he large scale of scenes, a sable and efficien approach for susainable sysems has o be eplored. Many analysis mehods on ranslaing videos have esimaed deph of scenes based on he srucure from moion using opical flow [15], feaure maching [4,16], and EPI racking [11,1]. Besides he compleiy issue of hese mehods, he enire video volume or image sequence are sored and processed, which makes he real ime processing difficul. On he oher hand, various laser range finders have been developed for scanning buildings. The shorcoming is he long measuring imes a a local posiion. The mos successful sysem for roue scanning is a vehicle-borne laser sysem [14] ha measures deph during movemen. In his work, we propose a unique mehod o measure he deph from a sree. We analyze a new phenomenon named saionary blur in he roue panorama and find he deph from he degree of he blur. The deph is esimaed from he emporal conras in he RP and he original spaial conras a he sli. More precise han he moion analysis based on absrac lines of sigh, he sampling Fig. 1 A secion of D roue panorama generaed from a sli wih is il slighly facing up.

2 process is analyzed. We propose an algorihm ha generaes more accurae deph bu uses much less daa han he srucure from moion. The sored compac daa for deph along wih he coninuous RP faciliaes he pos model consrucion along long roues. We generae planar model of srees for fas VR environmen consrucion. Relaed works for 3D scenes require feaure maching on wo GPVs [1] or image paches [4] scanned in differen direcions from he pah, which are influenced by repeiive paerns on buildings, and frequen occlusions from close range objecs. Anoher problem of hese mehods is he inconsisen coverage a disance scenes. Alhough wo close viewing direcions can diminish he difficuly in maching, he resuling deph of urban scenes has inadequae resoluions. An alernaive deph esimaion mehod is o rack EPIs in a video volume during he camera ranslaion [11,1,13]. However, i was used a local posiions or for shor disances. On a real road, EPIs are easily desroyed by unsable camera moion or curved pahs. Robus racking in EPIs is hard o epec. Overall, hese approaches have no eended o a long disance. The difference of his work from ohers is in deermining he deph insanly from local daa, according o he saionary blur. We evaluae he blur o avoid he feaure maching or racking. A filering process makes he scanning suiable for real ime processing, robus o oudoor moion, and eendable o long disances. Raher han elongaing ime for more observaions, we generae deph direcly as he sli scans across a scene. Such a sraegy reduces he influences from he varied moion, vehicle shaking, and occlusion in urban environmens. I keeps he advanages of he roue panorama and will benefi sensor developmen. The moion is obained from oher reliable sources such as GPS or vehicle conrol. The model can be consruced by using he obained deph and moion informaion a a global level. As a base, we provide a general model of scanning in Sec., which covers sli seing, shape and moion aspecs of he roue panorama based on absrac lines of sigh. Saionary blur is eplored a a deailed sampling level in Sec. 3. Local and global esimaions of he deph from he saionary blur are in Sec. 4, and eperimen in Sec. 5.. Acquisiion of Roue Panoramas.1 Camera Moion and Sli Scanning We define a sli-scanning model ha is more general han wha has been proposed in [6]. A camera is mouned on a vehicle wih is ais perpendicular o he vehicle ranslaion V. The vehicle pah is a smooh curve on a horizonal plane, wih small disurbances over bumpy roads. A four-wheeled vehicle can realize his moion. We denoe he camera pah by S() in a global coordinae sysem, where is he ime of scanning. Such a pah is he envelope of circular segmens wih changing curvaure κ. If he vehicle moves along a sraigh lane, he camera pah has curvaure κ0. The vehicle keeps a speed as consan as possible. The variaion in speed and pah can be S() Image frame Camera focus Sli Camera pah Sli α Sli horizon Verical Plane of scanning Horizonal ais hrough sli α Sli Camera pah Fig. Camera moves along a pah on a horizonal plane. normalized by reading precise oupu from GPS as in [8]. In order o produce good shapes in he roue panoramas, a plane of scanning (PoS) is se verical in he 3D space as he camera moves forward (Fig.). This ensures ha verical lines in he 3D space appear verically in he RPs even if he camera moves on a curved pah. The curves or lines parallel o he camera pah are horizonal in he roue panorama. The angle α beween he PoS and moion vecor V deermines he viewing aspecs of srees, e.g., side view, fore-side view, or rear-side view along he sree. The camera frame inersecs wih he PoS o form a sli l. The sli may no be verical in he frame if he camera is iled up for high buildings (Fig. 3). We calibrae he sli by using a sample image aken when he vehicle is on a horizonal plane. A building wih verical srucure lines eiss in he image. The vanishing poin of he verical lines is esimaed and he sli is programmed o pass he vanishing poin for he verical PoS. From he vanishing poin, we calculae he camera il, and hen he projecion of he horizon h in he image. Because of he defined camera direcion, opical flow caused by moion V is horizonal for he local ranslaion. For mos curved pahs V Parallel Planes of scanning and parallel sli views Roue Panorama

3 wih low curvaures, he flow direcion on he sli can sill be approimaed as horizonal. We coninuously collec he emporal daa on he sli (one piel line) and pase hem o anoher image memory consecuively. The generaed roue panorama has ime (frame) as is horizonal coordinae and sli y as is verical coordinae. A fied sampling rae, normally seleced as he maimum reachable frame rae, is used for scanning. The generae roue panorama, which has non-redundan scene coverage, is only a slice in he enire video volume. Concurren o he acquisiion of he roue panorama, we compue he spaial differenial across he sli (involving ± piels) for laer use. Vanishing Poin Image Horizon Horizon Opical Flow Sli1 Sli Sli3 Fig. 3 Eample of he programmable slis in he image frame deermined from he vanishing poin of verical lines. The sli scanning approach obains a D roue panorama direcly. I processes less daa han he image siching because no scene overlapping and iner-frame maching are necessary in he scanning. Mosaicing for a ranslaing camera is no as simple as for a roaing camera. Because he ranslaion yields inconsisen moion paralla a differen dephs, a perfec D image overlapping is impossible. Deforming scenes o a dominan deph yields an irregular scale in he image lengh; he horizonal coordinae is no longer equal-disance bu dephdependen. The real inegraion of paches has o be done in 3D space, or equivalenly piece-wised image deformaion [4]. The mosaicing requires image maching, moion esimaion, and inermediae view inerpolaion, which may be influenced by occlusion and lack of feaures. If hese are no applied a he sensor level, he cos o sore he increasing images during he vehicle moion is huge.. Shape Properies of Roue Panoramas From he projecion poin of view, scenes a consecuive ime insances are projeced along he fied PoS owards he camera pah. A parallel-perspecive projecion is obained from linear camera moion, and a bendedparallel-perspecive projecion from curved pah. As depiced in Fig., scenes scanned by one PoS are projeced ono he sli view in perspecive projecion, and enire scenes are projeced owards he camera pah hrough he parallel planes of scanning wih he same angle α from V. By ransforming daa on a sli l o he sli l verical in he PoS, we can conver a general roue panorama o he basic roue panorama ha is verical along he camera pah. Defining he local camera coordinae sysem O-XYZ, which has he X ais aligned wih V, and verical Y ais, a 3D poin P(X,Y,Z) under he normal perspecive projecion has he image posiion (,y) as I (, y, ) : Xf Z, y Yf Z (1) where f is he calibraed camera focal lengh. The projecion of P in he roue panorama hrough he parallelperspecive projecion is I, y) I(, y, ) ( ' l I (, y) : S r, y Yf Z, r V m () where V V, S S, m (frame/sec) is he camera frame rae, and r (meer/frame) is he sli sampling inerval on he pah. The parallel-perspecive projecion is differen from eiher perspecive projecion or parallel projecion. Under perspecive projecion, boh horizonal and verical scales of an objec are relaive o he deph. While in parallel projecion, boh scales are absolue. Under parallelperspecive projecion, he horizonal scale is absolue while he verical scale is relaive o he deph. Therefore, objecs in he roue panorama have shape characerisics briefly as follows: (1) A disan objec looks wider in a roue panorama han in a perspecive image, and looks lower han in a parallel projecion image. () Due o he parallel PoS piled horizonally, a 3D line sreching in deph is projeced as a hyperbola approaching o a horizonal asympoic line; such a line in a perspecive image is generally slaned and eends o a vanishing poin. (3) Under bended-parallel-perspecive projecion of scenes owards a general curved pah, 3D lines will be projeced as lines or envelopes of hyperbolas in he roue panoramas..3 Moion Characerisics a Sampling Sli Assume poin P(X,Y,Z) has ranslaion V(V,0,0) and roaion Ω(0,β,0), where roaion velociy β is a piecewised consan along a pah readable from GPS. The relaive velociy of he poin o he camera is P ( ) V + Ω P ( ) (3) When he poin is viewed hrough he sli a ime, he above can be decomposed o X ( ) Y ( ) Z( ) V + βz( ) 0 βx ( ) (4) Taking derivaive of (1), he image velociy v a he sli

4 posiion is Z( ) / Z( ) v f fx f (5) Z( ) Z ( ) Z( ) Z( ) For he linear moion, β0 so ha V Z ( ) f fv (6) v which is a radiional approach o obain deph. In order o obain good esimaion of v, feaure maching, EPI racking and Kalman filering have been applied o he enire image sequence over a long observaion period. For he sli scanning, however, no redundan daa are acquired for racking and posiion refinemen. The deph has o be compued from he sli and a few addiional piels around i. We achieve his goal by using he saionary blur in he roue panorama. 3. Saionary Blur in Roue Panorama 3.1 Saionary Blur as Counerpar of Moion Blur We find ha disance feaures in he roue panorama have blur along he direcion in he roue panorama. This phenomenon is paricularly obvious in he sree scanning of urban areas. By eamining he mechanism of his blur, we find i has similar characerisics as he moion blur bu appears in he emporal domain. We name i saionary blur because i appears on poins wih low image velociies and, herefore, reaining a he sli posiion. The slower he camera velociy, he more he saionary blur is visible. If he camera sops, idenical scenes are projeced ono he sli so ha horizonal paerns las along he ime ais in he roue panorama. This is similar as he moion blur sripes along he opical flow direcion in he spaial domain, if he camera velociy is eremely fas. I is well known ha, under perspecive projecion, he image velociy is proporional o he camera ranslaing speed and inversely proporional o he deph. For linear moion, saionary blur and moion blur appear in differen ranges. If we illusrae he moion in EPI (Fig. 4a), he race of a close feaure may sweep across several ( ) piels in he image during he camera eposed ime τ. The refleced ligh from he feaure hus conribues o he inensiies of muli-piels in he image frame. Accordingly, he inensiy colleced a a piel is he average from several neighboring poins in he scene. The image inensiy is obained from he convoluion beween he surface color and a recangular pulse. If he surface poin is an edge, his average yields a moion blur [9]. A close feaure wih a high image velociy has severe moion blur [18]. On he conrary, a slow-moving poin in he field of view reains a he same image posiion in several sampling insances ( ), and is repeaedly capured by he sli (Fig. 4b). This causes he saionary blur along he ais in he roue panorama. Disan objecs appear o be saionary-blurred because of heir slow image velociies. Moion blur and saionary blur are no only relaed o deph. A conve pah may produce more moion blur on objecs over all ranges because of he addiional roaion velociy, while a concave pah may produce more saionary blur a objecs close o he cener of curvaure of he pah. EPI One piel Moion race of poin Fig. 4 Traces of fas and slow moving feaures in he EPI, which cause he moion blur in he image and saionary blur in he RP, respecively. 3. Formaion of Saionary Blur in RP Using he projecion model discussed in Sec., he sli widh is ideally zero, he PoS is absoluely hin, and he sampling is infiniely dense on he camera pah. In he real siuaion, he sli has a nonzero physical widh and he roue panorama is formed wih narrow perspecive projecions (Fig. 5). Differen dephs, classified as jussampling deph, under-sampling range and overlappedsampling range, have differen sampling characerisics. For scenes a he jus-sampling deph, is views capured from consecuive slis can be conneced wihou overlapping jus as a normal perspecive projecion. A a deph closer han he jus-sampling deph, consecuive sli views canno cover a space compleely and he scene is under-sampled. On he conrary, a poin farher han he jus-sampling deph may be covered by muliple sli views, which is an overlapped-sampling. The poin color conribues o muliple slis over ime, which is an aliasing in he ime domain. If he poin is an edge, is inensiy change repeas along he ime ais and resuls in a lower conras in he roue panorama han in he image. We should no simply squeeze he roue panorama along ime ais o reduce he saionary blur on disance scenes, because his may deform a close scene a he same place. Alhough i has no been menioned ye, he mosaicing doing local image deformaion (epansion or squeezing) also has resoluion changes similar as he saionary blur and he under-sampling. The degree of he blur is relaed o deph Z, camera sampling rae m, pah curvaure κ, and vehicle speed V. If m and V are invarian locally over a linear pah (κ0), we can esimae he deph from he saionary blur. EPI One piel

5 Z j Sli Fig. 5 Real projecion of a roue panorama (op view) by consecuive perspecive cones disribued along he camera pah. F( Scene color disribuion PSF( H W comb( X i X i+1 Averaging funcion Sampling funcion Overlappedsampling range Jussampling Under-sampling range Camera Pah X X comb ( X ) δ ( X W ) (10) I and he image a he sli is I( ) IM( comb ( PSF( F( X W w / w / I δ ( X W ) F ( X + s) PSF ( s) ds (11) From I(), we furher approimae emporal differenial I()/ by emporal difference I(). Assume surface color F( a deph Z has a disconinuiy observable in he image (a large feaure) as depiced in Fig. 7. The horizonal difference of he roue panorama is I ( ) H w/ w/ / w ( F( s + ( + 1) r) F( s + ( 1) r)) ds F H r ds (1) / w On he oher hand, he spaial difference I() a sli is I ( ) H H w/ w/ / w / w ( F( s + ( + 1) W) F( s + ( 1) W)) ds F W ds (13) Fig. 6 Sampling roue panorama. X I() Edge profile on surface I() In deermining an inensiy a a sli, colors of surface poins covered by a Poin Spread Funcion (PSF) are averaged. We approimae Gaussian PSF by a recangle pulse wih heigh H and span W normalized by W H1. The span W of he PSF a a cerain deph Z is WZanθ, where θ is half of he angle subended by he cone. The averaging of scene color a Z corresponds o convolving color F(, which is unknown, wih he pulse PSF(, i.e., I ( X ) PSF ( X ) F( X ) (7) where denoes he convoluion. This phase is no differen from normal perspecive projecion. In he sampling phase of he roue panorama, he funcion comb p ( X ) δ ( X r) (8) wih deph-independen inerval r acually samples I(, where δ is he impulse funcion. The inensiy I() in he roue panorama is finally obained by I( ) RP( comb ( PSF( F( X r w / w / p δ ( X r) F ( X + s) PSF ( s) ds (9) for he symmeric PSF. As a comparison, sampling cones (PSF) of he perspecive projecion will no inersec each oher, i.e., he sampling funcion a deph Z is Z Fig. 7 PSF coverage of he roue panorama and he image a he same posiion on a pah. 4. Local and Global Esimaions of Deph 4.1 Local Esimaion Generaing Dense Deph Z j r Parallel-perspecive projecion X Perspecive projecion We firs eamine he deph esimaion from saionary blur a poin level. Alhough he degree of saionary blur is relaed o he deph, he conras disribuion in he roue panorama is insufficien o deermine he deph independenly because he original scene conras disribuion is unknown. To obain he spaial conras a he sli, we calculae differenial value in he images, as he roue panorama is eended. I/ is compuable a l if we wide he sampling sli o several neighboring piels. We calculae I()/ in he RP, and i reflecs he conras afer W

6 saionary blurring. The raio of he spaial and emporal differenials provides he deph as (6), because I (14) I and hus he deph can be esimaed by V I Z( ) f fv ( ) (15) v I for a linear pah. This is no used direcly so far in he opical flow for he well-known reason ha local opical flow can no generae accurae deph. Here we eamine he scope and resoluion of he filering for he local evaluaion of raio I and I. We use 3 5 Gaussian operaors o calculae he spaial and emporal differenials I (,y) (which is I (,,y) l ). This reduces noise from he roughness of he roue panorama. Esimaed Deph Esimaed Deph Ideal Deph Jus Sampling:8 Jus Sampling:16 Jus Sampling:3 Jus Sampling:64 Deph Disribuion Original Deph Ideal Deph Eposure Time:0.8 Eposure Time:0.5 Eposure Time:0.5 Eposure Time:0.1 Deph Deph Disribuion wih Moion Blur Fig. 8 Deph esimaion on synhesized daa. A sep edge is pu a all disances wih 1m inerval for measuring. (a) Differen jus-sampling dephs are se o esimae he dephs (colored curves). (b) Differen eposure imes are se o measure close dephs. A longer eposure ime produces a beer deph a close range. To es he algorihm, we locae an ideal sep edge a all he dephs and calculae is disribuion by Eq. 15 for a se of jus-sampling dephs (Fig. 8). The resuls show ha he deph around he jus-disance Zj is beer han dephs of disance scenes and very close scenes. The error a an approimae range of (Z j, ) is from he sysem when we choose a small operaor scope. Also, as he deph increases, he daa level becomes coarse. A he close range of (0, Z j /), he measured deph is apparenly more disan han is rue value. This is caused by he under sampling effec where he locaion of he edge canno be capured in consecuive slis compleely, which is also possible o be inerpreed by Nyquis heorem. Alhough reducing he jus-sampling deph may improve he measure a close disances, he maimum frame rae and he resoluion of he camera limis his possibiliy. On he oher hand, if we eend eposure ime of he frames (slow down shuer speed) o include he moion blur, he resul is improved clearly in Fig. 8b. This is because he PSF sweeps a wider area over he sampling inerval r, and he moion blur provides suble changes in he sli views for he evaluaion of spaial differenial. Using local daa o yield deph insanly can avoid many comple issues such as occlusion, dynamic objecs, and lack of feaure in oudoor scene. In he real roue panorama, oher wo feaure selecion crieria are added. The original level of feaure conras affecs he resuling levels of I and I and hen heir raio. We selec reliable edges wih high conrass eiher in emporal or in spaial domain o calculae deph. A spaial-emporal gradien g(,y) no influenced by moion blur and saionary blur is calculaed as g(, y) ( I (, y)) + ( I (, y)) (16) for all y in he roue panorama. Feaures saisfying g(,y)>δ are seleced for deph esimaion. To avoid disurbance from feaures a differen heighs due o vehicle shaking and waving, we avoid near-horizonal feaures in he deph esimaion by limiing edge orienaions in he roue panorama. Figure 9 provides he resul of he poin-based deph measure, where spaial and emporal differenial images are generaed during inpu. The dephs for he qualified poins are compued. The raio in (15) is displayed in gray levels. The brigher he poin he closer he deph is, and vice versa. We can fill deph a empy poins by using linear inerpolaion horizonally. This deph migh be fine for visualizaion using layered represenaion, i is sill very noisy and canno yield saisfacory surfaces even we fi lines and planes over i. A he fron lawn wihou many feaures, he dephs are no reliable. 4. Deph from Global Measure of Blurs The mos significan resul of he deph from saionary blur mehod is a global measure of he blurring. Insead of averaging or voing noisy daa obained from local deph

7 o obain layered images, we use global measures of he spaial and emporal differenials separaely and hen esimae he reliable deph from heir raio. han he average of dephs from all poins. The average of he differenials can even be eended o irregular shapes afer segmenaion of he emporal roue panorama, which has never been ackled in he opical flow compuaion. The resuling surfaces have more precise dephs han layers (Fig. 11). 5. Eperimens and Discussion Fig. 9 A secion of roue panorama and is poin based deph esimaion. (a) RP (b) Temporal differenial, (c) Spaial differenial, (d) Esimaed deph a poins wih high spaial-emporal gradien. The value of arcan(i/i) is displayed. Poins wihou deph measure are se o zero. Assuming an area σ in he roue panorama capures an edge in he 3D space, we compue he averages of he spaial and emporal differenials respecively from all poins wih srong g(,y)>δ in σ. According o (1) and (13), he raio of he wo averages is I g (, y ) >δ1 (, y ) I (, y) g (, y ) >δ1 w/ I ( ) I ( ) (17) 6. Conclusion w/ F F W CZ ds ds H r r w / w / where C is a consan and he resul is proporional o he deph. If an area is large o conain muliple feaures wih posiive and negaive differenial values, we calculae Zˆ We have driven a vehicle hrough many srees o record complee roue panoramas. For he roue panorama shown in his paper, he vehicle speed is 0km/hr. A fied sampling rae of he sli is se a 60HZ. On uneven roads, a vehicle suffers from addiional shaking. Alhough he lefand-righ ranslaion will no happen, he vehicle roll (lefand-righ swing) may have a big influence on he camera. Abrup verical ranslaion due o he disurbance in vehicle pich over a bumpy road will no be large and can be reduced by using a large vehicle wih a long wheelbase and sable suspension. The video camera used has a shaking compensaion funcion. A shaking removal algorihm has been developed o recify he roue panorama and hen he corresponding differenial images [19]. The moion blur may affec feaure maching in he srucure from moion. However, i does no influence he roue panorama, because close objecs wih high image velociies leave heir clear view in he RP. Our filering mehod use g(,y) ha is invarian o he moion blur in feaure selecion. The enire scanning keeps he calculaion of spaial differenial wihin a very narrow sripe (5piel for he operaor size) around he sli; i is much less han image paches used for feaure searching, maching, and siching. The deph can be generaed insanly because he algorihm uses a consan ime for local compuaion and he compleiy for he enire roue panorama is linear (O(S)). The sorage for he sree model generaion is only wo imageries (RP and spaial differenial images). I ( ) C I ( ) (18) for he deph. This raio of summarized differenials significanly reduces he noise and he resul is more sable This work developed he saionary-blur based deph esimaion for roue panoramas. Through an elaboraed analysis of blurs and moions, he proposed algorihm avoids feaure maching and racking in srucure from moion mehods. I can generae robus deph measure efficienly wihou being influenced by occlusion, moion blur, and oher comple siuaions. From he daa sorage perspecive, he spaial differenial and he roue panorama sored are much less han EPIs used in racking, and image paches used for siching. I keeps he daa compacness for sensor and sysem developmen. Moreover, he

8 coninuous and non-redundan roue panorama grealy simplifies he model generaion. I will broaden applicaions of sli scanning o real ime visual archiving of ciyscapes for communicaion and visualizaion. References [1] J. Y. Zheng, S. Tsuji, Panoramic represenaion for roue recogniion by a mobile robo. In. Journal on Compuer Vision, 9(1), 55-76, 199. [] J. Y. Zheng, S. Tsuji, Generaing dynamic projecion images for scene represenaion and undersanding, Journal of Compuer Vision Image Undersanding, 7, (3), 37-56, Dec [3] D. G. Aliaga, I. Carlbom, Plenopic siching: a scalable mehod for reconsrucing 3D ineracive walkhroughs, SIGGRAPH01, 001. [4] Z. Zhu, A. R. Hanson, E. M. Riseman, Generalized parallelperspecive sereo mosaics from airborne video. IEEE Trans. PAMI, 6(), 6-37, 004. [5] J. Y. Zheng, M. Shi, Mapping ciyscapes o cyber space, In. Conf. CyberWorld 003, pp , 003. [6] J. Y. Zheng, Digial roue panorama, IEEE Mulimedia, 10(3), 57-68, 003. [7] T. Kawanishi, K. Yamazawa, H. Iwasa, H. Takemura, N. Yokoya, Generaion of high-resoluion sereo panoramic images by omnidirecional imaging sensor using heagonal pyramidal mirrors, 14h ICPR, Vol. 1, pp , [8] S. Li, A. Hayashi, Robo navigaion in oudoor environmens by using GPS informaion and panoramic views, Proc. IEEE/RSJ In. Conf. on Inelligen Robos and Sysems, pp , [9] M. Ben-Ezra, S. K. Nayar, Moion deblurring using hybrid imaging, CVPR03, , 003. [10] D. Zome, D. Feldman, S. Peleg, D. Weinshall, Mosaicing new views: he crossed-slis projecion, IEEE Trans. PAMI, pp , 003. [11] H. Baker, R. Bolles, Generalizing epipolar-plane image analysis on he spaial-emporal surface, Proc. CVPR-88 pp.-9. [1] C-K. Tang, H-Y. Shum, Efficien dense deph esimaion from dense muli-perspecive panoramas, ICCV 001. [13] Z. Zhu, A. R. Hanson, 3D LAMP: a new layered panoramic represenaion,, , ICCV 001. [14] H. Zhao, R. Shibasaki, A Vehicle-borne urban 3D acquisiion sysem using single-row laser range scanners, IEEE Trans. on SMC, B: 33(4), 003. [15] S. Srinivasan, Eracing srucure from opical flow using he fas error search echnique, IJCV, Vol. 37, 03-30, 000. [16] J. Weng, Y. Cui, N. Abuja, Transiory image sequences, asympoic properies, and esimaion of moion and srucure, IEEE Trans. PAMI, 19(5), [17] A Wang, E. H. Adelson, Represening moving images wih layers. IEEE Trans. Image Processing, 3(5):65-638, [18] J. S. Fo, Range from ranslaional moion blurring, IEEE CVPR88, pp , [19] J. Y. Zheng, Sabilizing roue panorama, 17h ICPR, , 004. Fig. 10 Roue Panorama and esimaed deph mpa. (a) roue panorama, (b) deph map afer filling empy holes wih neighboring measured poins, (c) an enlarged secion of deph map. Fig. 11 Surface model of a sree from global paches afer segmenaion.

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