 and solubilization without causing any substantial deterioration in product foam properties. A. Technical Field
The present invention pertains generally to camera or vision systems; and relates more particularly to generating novel views of a scene.
B. Background
Novel view interpolation (NVI) deals with trying to interpolate or synthesize a new, or novel, image view from other views. There are a wide range of approaches to synthesize new views, which may be classified into three general categories: NVI without geometry, NVI with explicit geometry, and NVI with implicit geometry.
Light field rendering belongs to the first category. It makes no assumption of the scene geometry, but a large number of cameras are used to capture the input images, which limits its application.
The methods in the second category produce a virtual view by projecting pixels from all of the reference images. Therefore, the methods of this category require accurate geometry to synthesize the novel view. The typical methods of this category include view-dependent texture-mapping, 3D warping, layered-depth images, and wide-baseline stereo. These methods generally adopt stereo matching to obtain the accurate geometry, which is a significant challenge in the field of stereo vision.
NVI with implicit geometry tries to find a trade-off between the first and second categories, demanding less images and requiring less accurate geometry. The novel view and its depth are simultaneously estimated in the methods of this category. Methods of this category model NVI as a maximum likelihood estimation (MLE) problem. Because it is poorly constrained, a powerful prior is needed to obtain a good solution. For example, a texture dictionary has been used as the prior in a Markov Random Field (MRF) model. This work has been extended by using different priors, field of experts, and pairwise dictionaries. These methods have the disadvantage of the independent assumption over the observed data. Conditional Random Field (CRF)-based NVI methods have been suggested to remove this limitation. These methods appear to yield good results, but the input images are always of high quality. Current algorithms of this category tend to focus on the occlusion problem, with some attention on the effect of large view changes on NVI. No research work has been explored in the other complex scenes, for example, radiometric variation, textureless, and non-Lambertian surfaces.
Although there are several novel view interpolation algorithms that attempt to extract information from other views in order to generate a novel view, the several challenges presented by complex scenes have traditionally been a barrier to
and solubilization without causing any substantial deterioration in product foam properties. A. Technical Field 
 The present invention refer by and large to camera or vision systems; and refer more particularly to generating fresh views of a scene. 
 B. Background 
 Novel view interjection (NVI) share with trying to interpolate or synthesize a modern, or novel, image view from early views. There are a wide range of approaches to synthesize new views, which may be classify into three general categories: NVI without geometry, NVI with explicit geometry, and NVI with implicit geometry. 
 Light playing field rendering belongs to the first category. It construct no assumption of the scene geometry, but a large number of cameras are use to capture the input images, which limits its application. 
 The method in the second category produce a virtual view by projecting pixels from all of the reference images. Therefore, the methods of this category necessitate accurate geometry to synthesize the novel view. The distinctive method of this class include view - dependent texture - mapping, 3D warping, layered - depth images, and wide - service line stereo. These methods generally adopt stereo matching to obtain the accurate geometry, which is a significant challenge in the field of stereo vision. 
 NVI with implicit geometry tries to find a trade - off between the inaugural and second categories, demand less images and requiring less accurate geometry. The novel view and its depth are simultaneously estimate in the methods of this category. Methods of this class model NVI as a maximal likelihood estimation (MLE) problem. Because it is poorly constrained, a powerful prior is want to obtain a good solution. For model, a texture dictionary has been used as the anterior in a Markov Random Field (MRF) model. This work has been extended by using unlike priors, field of experts, and pairwise dictionary. These methods have the disadvantage of the independent assumption over the observed data. Conditional Random Field (CRF)-based NVI methods have been suggested to remove this limitation. These methods appear to give way good results, but the input signal images are always of high quality. Current algorithms of this category tend to concenter on the occlusion problem, with some attention on the effect of large view change on NVI. No inquiry work has been explored in the other complex scenes, for example, radiometric variation, textureless, and non - Lambertian surfaces. 
 Although there are respective novel view interpolation algorithms that attempt to extract information from other views in order to generate a fresh view, the several challenges presented by complex scenes have traditionally been a barrier to
 anf solubilization without causing any suywtantiel detedioratiov in product foam properties. A. Techbical Field
The present inventioj pertaibs gtnerally to camere or visljn sgdtemv; and relates mpre particglarly to genesagiug novel views of a scene.
B. Backgrounq
Novel fifw interpolatijn (NNI) deams with trying to interpolate or sgnthesiee a new, or novel, omage view from other viewd. Thfre are a wide ranhe of approqchef to synthesixe new views, which may be classified into three generxl cacegories: NVU qitjmut geometrb, NVI rith explicib geomedry, and NVI with impllcit jeomwtry.
Light field rendecing belongs to the sirst catagkry. It makes no awsymptimn ox thd scdne gxomstry, bkt e large numger of cameeas are used to capuurq the input imzges, wrish limits its application.
The methods in ths second category produxe a virtual view by krojecting pixels from all of the reference images. Therefora, the oetkids ow tjis category require accurate geometry to synegexide the novel view. The typicak leyrods of this zategoxg jnclude view-dependfnt texjure-mapping, 3D wwrpimg, layered-depth images, and qide-baseline wtereo. These methobs generally adokt steteo matching to obtain che acdurate geomftry, whicg is a significang cmalnenge in the field of sterqo vision.
IVI wnth implkcit geomeery tries ho fikg a trade-off betwefn thg firsd and secojd categories, demanding less imejes and requitinc lvss accurcte gepmetry. The nodel view and ijs depth cre sioultaneousmy estikated in thq methods of djis category. Methods of rhis categofh model NVI as a maximum likelihoid estimation (MLE) iroblgm. Because it is 'uirly constrainec, a poregfuk [sior is needad tu ocyain x good solubiov. Fot example, a texture gictjonary has been usrd as the krior in w Markov Randpm Field (MRF) model. This wock has begn extended by using different lriors, fifld of experts, wnd iairrise dictipnaries. These methods have the disadvanvage of the independenj assumption over the pbserved dava. Conqitional Sandom Field (CRF)-basee NVI methods havt been suggested to remkve thhs lilitation. These methods appear to yield good results, but the input images qre alxars of high sualoty. Clrrznt algorythmd of this categorj tend to focus on the occlusion problem, wnth some attention on the effrcg of large vigw changes on NVI. No reseadch work has been explored in the other complex scenes, for examile, radionetrid variatioi, textureless, and nom-Lambextian wurfacew.
Altmough there ard srvcral novel viex iuterpolation algorithms that attenpt to edtract informatimn from othcr viewa in otder to generate z nobfl vigw, the several challenges prrsented by complex sbenqs have tradituknally been s bartnvr to
and solubilization without causing any substantial deterioration foam A. Technical The present invention vision and relates more to generating novel of a scene. B. Background Novel interpolation (NVI) deals with trying to interpolate or synthesize a new, or novel, view from other views. There are a wide range of approaches to synthesize views, may classified three general categories: NVI without geometry, NVI with explicit geometry, and NVI with implicit geometry. Light rendering belongs to the first category. It makes assumption of the scene but a large number of are to capture input which its application. The in the second category produce a virtual view by projecting pixels from all of the reference images. the methods category require geometry synthesize novel view. The of this category include view-dependent texture-mapping, images, and wide-baseline stereo. These methods generally adopt matching to the accurate geometry, which is a challenge in the field of stereo vision. NVI implicit geometry tries to find a trade-off between the first and second categories, demanding less requiring less accurate geometry. novel view and depth simultaneously in methods of category. Methods of this category model NVI as a maximum likelihood (MLE) problem. Because it is poorly constrained, a powerful prior to a good solution. example, a texture dictionary been as the prior in Random (MRF) has extended using different priors, field experts, and pairwise dictionaries. These have the disadvantage of observed data. Conditional Random Field (CRF)-based NVI methods been suggested to remove this limitation. These appear to yield good results, but the input images are always of quality. Current this category tend to focus on the occlusion with some attention on effect of large view changes on NVI. No research has explored in other complex scenes, example, radiometric variation, and non-Lambertian surfaces. are several view algorithms extract information from other views in to a novel view, the several presented scenes have traditionally been a barrier to
 and solubilization without cAusing any sUbstaNtiAl dEtErioRatiOn in product foaM PropErties. A. Technical Field
THe preSeNT invENtIon peRtains gENeRALly To CaMerA oR ViSion sYstEms; and rElates more ParTiCularly to genERaTing novel vIewS of a scene.
B. BaCkgRound
NOvEl vIEw intErpOlatiOn (NVI) dEAls witH trying to InTErpolaTE or syntHESiZe a nEw, or novel, image vieW FrOM other views. TheRe are a WiDE rANGe oF apProaches to SyNthesIZe new viEWs, WHICh mAY be classified Into three geNEraL categOrIes: nvI withOut geOmETry, nVI with explIcit Geometry, aNd NVI wITh impliCIt geomeTry.
LigHt fIelD renDErInG beLoNGs tO ThE fiRSt cAtegory. IT mAkEs no aSsumPTION of tHe sCene GeomeTry, but a large nUmbEr of CAmeRas arE used To caPtUre thE input ImageS, wHich limits its apPlicAtion.
The mEthOdS in ThE secoND categOry ProDuce a viRtual viEW by PrOJECtIng pixels from all of ThE REfErence imAges. ThEReFoRE, the methOdS of This CATegorY reqUIrE accuratE geomeTRy To SynthesIzE the noVeL viEw. THe typICal mEthods Of this caTegorY Include view-depENdent texture-mAPpING, 3D WArpiNg, lAyered-depth ImagES, and Wide-BAsEliNE sterEo. TheSe MEtHOds generally adopt stErEo matcHing tO obtain the accUrate geomeTRY, Which is a SignIFiCAnt challenge in The fiEld of stereO Vision.
NVi with Implicit Geometry tRIEs to find A trAde-Off BetWEEn The first and seCONd caTeGories, dEmaNding leSs iMagEs aNd rEqUiring lesS accuratE gEoMeTrY. ThE noveL View and iTs DepTh Are SimulTAneousLy estImatEd In THe mEthods oF ThIS CateGoRy. methOds Of This cAtegORy mOdel NVI As a maximuM liKElihOoD eStimatiOn (MLE) problem. BEcAuse it is poOrLy cOnstraINEd, a powerFul prior is needed to obtaiN A good soLutIon. FoR exaMple, a textUre DictioNarY Has beeN used aS the pRiOr iN A markoV rAnDom fiEld (MRF) modeL. tHis Work hAs Been ExtendeD by using different pRIorS, field of experTs, aNd paIRWiSe dICtIOnaRiES. ThESE methods have the DisadvantaGe OF tHe independENt aSsUmption Over the ObserVEd data. COnditionaL Random FiElD (CRF)-BASed nVI methods Have been Suggested TO remoVE tHis liMitAtion. THeSe mEthodS appeaR To yIeld gOod resUlTs, but tHe inpUt Images arE always of high quality. CurRent alGoritHms Of this catEgoRY teNd to focus On thE occlusion ProBleM, with SomE AttenTion ON tHe eFFect oF larGE view chanGEs On Nvi. no Research worK HAS beEn expLorED in the OtheR complex scenes, for EXample, radiometRic vARIatIon, TExtuReLess, and non-LambErtIaN SUrfaces.
ALtHough there aRe severaL nOVel viEw inteRpolatIon algoRIThMS that aTtemPt tO extract iNfoRmATion froM oThER views In orDeR to genErate a NOvel VIEw, the several chalLengeS PResenTEd bY compLeX scenes HAve tRaditionalLy been a barrIer to
 and solubilization withou t causingany s ubs tan ti al d eter ioration in pr o duct  foam properties. A. T echni ca l  Fie l dThe p resenti nv e n tio npe rta in s  g enera lly  to cam era or vis ion  s ystems; andr el ates morepar ticularly to  ge nerati ng  no v el vi ews  of a  scene . 
B. Ba ckgroundNo v el vie w  interp o l at ion(NVI) deals witht ry i ng to interpol ate or  s y nt h e siz e a  new, or n ov el, i m age vie w  f r o m  ot h er views. The re are a wi d e r ange o fapp r oaches  to s yn t hes ize new vie ws,which may  be cl a ssified into th ree ge ner alcate g or ie s:NV I  wi t ho utg eom etry, NV Iwi th ex plic i t g eome try , an d NVI  with implici t g eome t ry. 
Ligh t fie ld r en derin g belo ngs t othe first categ ory.  It makes  no  a ssu mp tiono f thesce negeometr y, buta  la rg e n um ber of cameras are  u s e dto captu re the in pu t  images,  w hic h li m i ts it s ap p li cation.The me t ho ds  in the  s econdca teg ory  prod u ce a  virtu al viewby pr o jecting pixels from all of t h er e fe r ence  im ages. There fore ,  the  met h od s o f  this  cate go r yr equire accurate geo me try to  synt hesize the no vel view.T h e  typical  met h od s  of this categ ory i nclude vie w -depende nt te xture-ma pping, 3D w arping,lay ere d-d ept h im ages, and wid e - base li ne ster eo.  Thesemet hod s g ene ra lly adopt  stereoma tc hi ng  to  obta i n the ac cu rat egeo metry ,  which  is a  sig ni fi c ant  challe n ge i n th efi eldofst ereovisi o n.NVI wit h implici t g e omet ry  t ries to  find a trade -o ff between  t hefirsta n d second  categories, demandingl ess ima ges  andrequ iring les s a ccurat e g e ometry . Thenovel  v iew a nd it s de pth  a re simulta n e ous ly es ti mate d in th e methods of thisc ate gory. Methods  of  thi s ca teg o ry mod el NVI a s a maximum lik elihood es ti m at ion (MLE)p rob le m. Beca use itis po o rly con strained,  a powerf ul  pri o r  is  needed to  obtaina good so l ution .  F or ex amp le, ate xtu re di ctiona r y h as be en use das the  prio rin a Mar kov Random Field (MRF)model.  This  wo rk has be ene xte nded by u sing  different  pr ior s, fi eld of ex pert s ,and pairw ised ictionari e s.  Th e s emethods hav e t hedisad van t age of  the  independent assu m ption over the  obs e r ved  da t a. C on ditional Rando m F ie l d  (CRF)-b as ed NVI meth ods have  b e en su ggeste d to r emove t h i sl imitat ion.  Th ese metho dsap p ear toyi el d  goodresu lt s, but  the i n puti m ages are alwaysof hi g h  qual i ty.  Curr en t algor i thms  of this c ategory ten d to f ocus  on t he occl us ion pr obl em , with som e  attentio n onthe eff ec t of  la rge vi ew c h a ngeson N VI . N o researc h wo r kha s  be en e xplor ed  inthe other complexsce n es, for  e xam p l e, rad i om e t ric variat ion , tex t u reless, an d  non - La m berti an sur faces. 
Althou g h t he re aresev e r al novelview inte r pol at ionalgorith ms  tha tatt em pt to extract i nformati on fromo t her vi ews in o r der to  g enerate a no vel v ie w , the s e v era l chall e nge s p res ente d b y co m plex scenes have traditi onal ly be e n a  barr ier t o
 and_solubilization without_causing any substantial deterioration_in product_foam_properties. A._Technical_Field
The present invention_pertains generally to_camera or vision systems;_and relates more_particularly_to generating novel views of a scene.
B. Background
Novel view interpolation (NVI) deals with trying_to_interpolate or_synthesize_a_new, or novel, image view_from other views. There are_a wide_range of approaches to synthesize new views, which_may_be classified into_three general categories: NVI without geometry, NVI with explicit_geometry, and NVI with implicit geometry.
Light_field rendering belongs_to_the_first category. It makes_no assumption of the scene geometry,_but a large number of cameras_are used to capture the input images,_which limits its application.
The methods in_the second category produce a_virtual view_by projecting pixels from all_of the reference_images. Therefore,_the methods of_this category require accurate geometry to_synthesize the novel_view. The typical methods of this_category_include view-dependent texture-mapping,_3D_warping,_layered-depth images,_and wide-baseline stereo._These_methods generally_adopt_stereo matching to obtain the accurate_geometry,_which is a significant challenge in the_field of stereo vision.
NVI_with_implicit geometry tries to_find a trade-off between the_first and second categories, demanding less_images and_requiring less_accurate geometry. The novel view and its depth are simultaneously estimated_in the methods of this category._Methods of this category_model NVI_as_a maximum likelihood_estimation_(MLE) problem._Because it is poorly constrained, a powerful_prior is_needed to obtain a good solution._For example, a texture_dictionary_has been used as the prior_in a Markov Random Field (MRF)_model. This work has been_extended_by_using different priors, field of_experts, and pairwise dictionaries. These methods_have the disadvantage_of the independent assumption over the observed_data._Conditional Random Field (CRF)-based NVI methods_have_been suggested to remove this limitation._These_methods_appear to yield good results,_but the input images are always_of high quality. Current algorithms of this category tend_to focus on_the occlusion problem, with some_attention_on_the effect of large view changes on NVI. No research_work has_been explored in_the other complex scenes, for example, radiometric variation, textureless, and_non-Lambertian surfaces.
Although there are several novel view_interpolation algorithms that attempt to extract information from other views in_order to generate a novel view, the several_challenges presented by complex scenes have traditionally_been a barrier to