Aanæs, Henrik

Conradsen, Knut

Eiríksson, Eyþór Rúnar

Frisvad, Jeppe Revall

Kristensen, Rasmus Lyngby

Nielsen, Jannik Boll

Wilm, Jakob

In this positional paper, we discuss the potential benefits of using appearance models in additive manufacturing, metal casting, wind turbine blade production, and 3D content acquisition. Current state of the art in acquisition and rendering of appearance cannot easily be used for quality assurance in these areas. The common denominator is the need for descriptive and parsimonious appearance models. By ‘parsimonious’ we mean with few parameters so that a model is useful both for fast acquisition, robust fitting, and fast rendering of appearance. The word ‘descriptive’ refers to the fact that a model should represent the main features of the acquired appearance data. The solution we propose is to reduce the degrees of freedom by greater use of multivariate statistics

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Downloaded from orbit.dtu.dk on: Dec 21, 2017Quality Assurance Based on Descriptive and Parsimonious Appearance ModelsNielsen, Jannik Boll; Eiríksson, Eyþór Rúnar; Lyngby, Rasmus Ahrenkiel; Wilm, Jakob; Frisvad, JeppeRevall; Conradsen, Knut; Aanæs, HenrikPublished in:MAM2015: Eurographics Workshop on Material Appearance Modeling: Issues and AcquisitionLink to article, DOI:10.2312/mam.20151199Publication date:2015Document VersionPeer reviewed versionLink back to DTU OrbitCitation (APA):Nielsen, J. B., Eiríksson, E. R., Kristensen, R. L., Wilm, J., Frisvad, J. R., Conradsen, K., & Aanæs, H. (2015).Quality Assurance Based on Descriptive and Parsimonious Appearance Models. In MAM2015: EurographicsWorkshop on Material Appearance Modeling: Issues and Acquisition Eurographics. DOI:10.2312/mam.20151199Material Appearance Modeling Workshop (2015), pp. 1–4Holly Rushmeier and Reinhard Klein (Editors)Quality Assurance Based on Descriptive and ParsimoniousAppearance ModelsJ. B. Nielsen, E. R. Eiriksson, R. L. Kristensen, J. Wilm, J. R. Frisvad, K. Conradsen, H. AanæsTechnical University of DenmarkAbstractIn this positional paper, we discuss the potential benefits of using appearance models in additive manufacturing,metal casting, wind turbine blade production, and 3D content acquisition. Current state of the art in acquisitionand rendering of appearance cannot easily be used for quality assurance in these areas. The common denominatoris the need for descriptive and parsimonious appearance models. By ‘parsimonious’ we mean with few parametersso that a model is useful both for fast acquisition, robust fitting, and fast rendering of appearance. The word‘descriptive’ refers to the fact that a model should represent the main features of the acquired appearance data.The solution we propose is to reduce the degrees of freedom by greater use of multivariate statistics.Categories and Subject Descriptors (according to ACM CCS): I.4.1 [Image Processing and Computer Vision]: Dig-itization and Image Capture—Reflectance1. IntroductionMuch work has gone into formulating radiometric models ofsurface reflectance for believable photorealistic rendering ofmaterial appearance. This has led to a number of physicallyplausible models with intuitively meaningful parameters thatare appropriate for direct manipulation [MHH∗12]. In thispositional paper, we discuss the use of appearance models ina different context, namely in quality assurance of physicaland digital products. We argue that this area of applicationrequires models with few parameters, or parsimonious mod-els. Through our example use cases, we further argue thatthere is a significant need for such parsimonious models, andthat effort should be put into their development.The need for parsimonious radiometric models manifestsitself when we need to estimate the radiometric properties ofsurfaces in practice, e.g. when doing industrial inspection toensure that the products have the specified visual properties,or when we would like to acquire photorealistic models fromimages. In such cases, the number of measurements is lim-ited, maybe 5 to 20 per surface patch. This should be seen inlight of the number of measurements needed to reliably esti-mate a general bidirectional reflectance distribution function(BRDF). A BRDF is modeled by a 4D manifold and is typi-cally measured using a spherical gantry (a gonioreflectome-ter). This means that a very large number of measurementsis required, which in many cases is practically infeasible.According to the philosophy associated with Occam’s ra-zor, the formulation of descriptive and parsimonious modelswill also force us to better model and understand the under-lying radiometric phenomena. Thus, in the end, our modelsshould hopefully lead to physically plausible models withfew intuitively meaningful parameters as is needed for themore classical applications of appearance models. We be-lieve that it is possible to make large advances in this di-rection, meaning that the task of formulating parsimoniousmodels does not seem to be a frugal one.2. Relating to existing modelsPrevious work has shown that the classical empiricallyand physically based computer graphics reflectance mod-els cannot fit all measured reflectance data well [NDM05].This has led to a quest for models that provide a betterfit [BSH12, LKYU12]. The cost of a better fit is an increasein the number of model parameters, and the simplest model(the Phong model [Pho75]) already has two parameters percolor band and one parameter to describe the material glossi-ness. As such, the simplest model requires at least sevenmeasurements although ideally many more to robustly fitmeasured reflectance data. In applications of real-time re-flectance acquisition, this quickly becomes infeasible.The fitting of most parametric models is far from triv-submitted to Material Appearance Modeling Workshop (2015)2 J. B. Nielsen et al. / Descriptive and Parsimonious Appearance ModelsFigure 1: Frog printed out of Polylactic acid (PLA) plasticusing a Fused Deposition Modeling (FDM) printer.ial. Major challenges include determining what optimizersto use and what objective functions they should minimize.For the latter, various suggestions have been proposed in-cluding L1 minimization [NFCA14] and log-transformationwith cosine-weighting of observed data [NDM05]. Toaddress the issues of non-linear model fitting, alterna-tive approaches have been proposed where reflectanceis modeled by linear combinations of basis functions.Suggestions to basis functions include spherical harmon-ics [WAT92], wavelets [SS95], and densely sampled refer-ence reflectances [MPBM03]. The advantage here is thatfitting models to observations becomes extremely easy asthis corresponds to solving a linear system of equations. Thechallenge however, which is an unsolved problem, is identi-fying a sparse set of basis functions that model a wide vari-ety of material appearances well. We need, as a community,to work on this.3. Relevant CasesTo argue relevance, we now describe four cases where wehave identified that the current radiometric models or acqui-sition methods simply do not suffice. The cases are (1) ad-ditive 3D printing, where the 3D microstructures caused bythe printing process cannot be modeled well by standard re-flectance models; (2) real-time monitoring of reflectance inmetal production; (3) estimation of surface reflectance onmassive objects (wind turbine blades); and (4) reflectancemodels to be used with 3D scanners to allow simultaneousacquisition of geometry and appearance. These are all prob-lems that cannot be solved by conventional methods.3.1. Additive ManufacturingFor the past decade, additive manufacturing (3D printing)has been an accepted production method. Today, it is pos-sible to manufacture products in multiple materials rang-ing from soft polymers to metals [WC13]. A rapidly grow-Figure 2: Example of iron casting [VSRT15], where themould has introduced a surface roughness affecting the vi-sual appearance of the product. Image is courtesy of NikolajKjelgaard Vedel-Smith.ing market of internet printing services is emerging (shape-ways.com and i.materialise.com, for example) where userscan upload their own 3D models for printing. Fast and real-istic material rendering is of great interest to these types ofservices, allowing users to previsualize the printed outcomeof their models prior to committing to purchase. However,accurately obtaining these radiometric models is a challenge.The layer-like nature of the printing process yields surfaceartifacts, the most prominent known as the ‘staircase effect’which drastically alters material appearance for some mate-rials. Visually, we observe this as a local anisotropy, oftencorrelated with the surface curvature, see Figure 1. Thus theprinting process itself must be considered when producingan accurate model of the printed appearance.Radiometric model acquisition also has an applicationin the quality assurance aspect of additive manufacturing.So far, most effort has been placed on in-line geometricverification of parts [HNRP14, PH14] and color verifica-tion [EPA15]. These optical systems capture each and ev-ery layer during the print in order to verify its correctness.Combinations of such systems along with rapid radiometricacquisition could prove beneficial as slight deviations fromthe material optical properties could indicate failure due toe.g. overheating (color change) or structural collapses (sur-face normal orientation). In essence, we need to verify thequality of 3D prints, but practical constraints limit the num-ber of measurements that it is possible to acquire.3.2. Metal CastingMetal casting is still an actively used production method.Casting allows for the creation of seamless and rigid struc-tures in various materials. However, post machining of saidsubmitted to Material Appearance Modeling Workshop (2015)J. B. Nielsen et al. / Descriptive and Parsimonious Appearance Models 3Figure 3: Wind turbine blade right after molding.objects is in many cases required due to the rough surfacetexture resulting from the casting process, see Figure 2. Mea-surements of surface roughness parameters are useful for in-dustry and academia in order to optimize the casting proce-dure as it is related to the overall cast quality. Obtaining sur-face roughness parameters from optical reflectance is thusof great interest and is an active field of research [NTH13].As in the case of additive manufacturing, we see a sce-nario where practical constraints limit the feasible numberof measurements, thus creating a demand for accurate parsi-monious reflectance models that enable robust fitting.3.3. Wind Turbine BladesOne of the most important steps in quality inspection of windturbine blades is to find transverse folds in their longitudinalfiberglass mats. The longitudinal mats run all the way fromthe root of the blade to the tip and provide the blade with thebulk of its rigidity and strength. Multiple layers of longitu-dinal mats are needed to provide the necessary strength, andthe load must be evenly distributed across the layers. If onelayer has a fold, that layer will be tightened harder than therest of the layers, thus carrying more load when the bladeis being operated. Over time, this increased load will wearthe fold-layer down to the point where it snaps and therebycompromises the entire structure of the blade. Usually, thissudden release of tension creates a force on the remaininglayers so that these also snap. The result is a broken blade.Before painting, wind turbine blades are translucent dueto their composition of transparent epoxy resin and fiberglass(see Figure 3). A fold on a fiberglass mat will create a bulgebeneath the surface which alters the optical properties of thematerial. Currently, specially trained quality engineers shinepowerful light parallel to the surface and look for changes inthe reflections. An accurate automated measure of surfaceBRDFs could increase the efficiency and accuracy of thequality assurance by transforming the fold inspection froma qualitative process into a quantitative process.Figure 4: Structured Light system scanning a statue.3.4. Creating 3D ContentOptical 3D scanners are actively used throughout variousfields such as archaeology, biology, production, entertain-ment, medicine, and art. All aiming to capture high reso-lution 3D models in a relatively short amount of time. How-ever, in order to produce realistic and applicable digitizationof scanned objects, their radiometric properties must also bedetermined. Many commercial systems provide the abilityto capture surface textures in order to provide more aes-thetically pleasing models, but are often limited to assumingLambertian behaviour or at most a simple parametric model,such as Phong [Pho75] or Ward [War92]. As indicated inSection 2, these models fail to fit the reflectance propertiesof many real-world materials. Trouble is that we cannot im-prove the fit by increasing the number of model parametersas we need to acquire reflectance properties at speeds com-parable to the 3D scanning process. This underlines the needfor descriptive and parsimonious appearance models.An interesting property of structured light (SL) scannersis the fixed angle between observer (camera) and lightsource(SL projector). This is illustrated in Figure 4. Using only ex-isting components of such a setup thus poses a constrainton the observable regions in the BRDF domain. Likewise,the geometry also dictates illumination and view directionsrelative to the surface normal. Hence, again we see a practi-cal limitation on the available observations, which causes ademand for parsimonious models that enable robust fitting.4. DiscussionFrom the above, it is evident that there are number of rele-vant cases where today’s methods do not suffice. We believethat the problems in the mentioned cases can be solved, butthat they require us to approach material appearance model-ing from a new angle. Specifically, we believe that data anal-ysis and multivariate statistics should be involved more thanwe see it today, and also that we should introduce strongerpriors on the data. Such tools are necessary to considerablysubmitted to Material Appearance Modeling Workshop (2015)4 J. B. Nielsen et al. / Descriptive and Parsimonious Appearance Modelsreduce the degrees of freedom in the problems. A solution ofthis kind will greatly contribute to streamlining and automat-ing the entire production pipeline, which is an essential partof agile product development.Conclusively, we would like to reiterate that descriptiveand parsimonious reflectance models seem indispensable ifwe are to use material appearance models in the context ofquality assurance of printed, molded, and digitized products.References[BSH12] BAGHER M. M., SOLER C., HOLZSCHUCH N.: Ac-curate fitting of measured reflectances using a Shifted Gammamicro-facet distribution. Computer Graphics Forum (Proceed-ings of EGSR 2012) 31, 4 (June 2012), 1509–1518. 1[EPA15] EIRIKSSON E., PEDERSEN D., AANAES H.: Predict-ing color output of additive manufactured parts. In Proc. ASPESpring Topical Meeting on Achieving Precision Tolerances inAdditive Manufacturing (26-29 April 2015, Raleigh, North Car-olina, USA) (2015), pp. 95–99. 2[HNRP14] HANSEN H., NIELSEN J., RASMUSSEN J., PEDER-SEN D.: Performance verification of 3d printers. In Proc. ASPESpring Topical Meeting on Dimensional Accuracy and SurfaceFinish in Additive Manufacturing (13-16 April 2014, Berkeley,CA, USA) pp (2014), pp. 104–107. 2[LKYU12] LÖW J., KRONANDER J., YNNERMAN A., UNGERJ.: BRDF models for accurate and efficient rendering of glossysurfaces. ACM Transactions on Graphics 31, 1 (January 2012),9:1–9:14. 1[MHH∗12] MCAULEY S., HILL S., HOFFMAN N., GOTANDAY., SMITS B., BURLEY B., MARTINEZ A.: Practical physicallybased shading in film and game production. In ACM SIGGRAPH2012 Courses (2012), no. 10. 1[MPBM03] MATUSIK W., PFISTER H., BRAND M., MCMIL-LAN L.: Efficient isotropic BRDF measurement. In RenderingTechniques (Proceedings of EGSR 2003) (June 2003), pp. 241–247. 2[NDM05] NGAN A., DURAND F., MATUSIK W.: Experimentalanalysis of BRDF models. In Rendering Techniques (Proceed-ings of EGSR 2005) (2005), pp. 117–126. 1, 2[NFCA14] NIELSEN J. B., FRISVAD J. R., CONRADSEN K.,AANÆS H.: Addressing grazing angle reflections in phong mod-els. In SIGGRAPH Asia 2014 Posters (2014), ACM, p. 43. 2[NTH13] NWAOGU U. C., TIEDJE N. S., HANSEN H. N.: Anon-contact 3d method to characterize the surface roughness ofcastings. Journal of Materials Processing Technology 213, 1(2013), 59–68. 3[PH14] PEDERSEN D., HANSEN H.: Comparability of the perfor-mance of in-line computer vision for geometrical verification ofparts, produced by additive manufacturing. In Proc. ASPE SpringTopical Meeting on Dimensional Accuracy and Surface Finish inAdditive Manufacturing (13-16 April 2014, Berkeley, CA, USA)pp (2014), pp. 179–183. 2[Pho75] PHONG B. T.: Illumination for computer generated pic-tures. Communications of the ACM 18, 6 (1975), 311–317. 1,3[SS95] SCHRÖDER P., SWELDENS W.: Spherical wavelets: Effi-ciently representing functions on the sphere. In Proceedings ofthe 22nd annual conference on Computer graphics and interac-tive techniques (1995), ACM, pp. 161–172. 2[VSRT15] VEDEL-SMITH N. K., RASMUSSEN J., TIEDJE N. S.:Thermal distortion of disc-shaped ductile iron castings in verti-cally parted moulds. Journal of Materials Processing Technology(2015), 262–271. 2[War92] WARD G. J.: Measuring and modeling anisotropic re-flection. In SIGGRAPH 92 (1992), pp. 265–272. 3[WAT92] WESTIN S. H., ARVO J. R., TORRANCE K. E.: Pre-dicting reflectance functions from complex surfaces. ComputerGraphics (Proceedings of ACM SIGGRAPH 92) 26, 2 (July1992), 255–264. 2[WC13] WOHLERS T., CAFFREY T.: Additive manufacturingand 3d printing state of the industry annual worldwide progressreport. 2014. Wohlers Associates (2013). 2submitted to Material Appearance Modeling Workshop (2015)

Quality Assurance Based on Descriptive and Parsimonious Appearance Models

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Quality Assurance Based on Descriptive and Parsimonious Appearance Models

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