Adaptve Selecton of Renderng Prmtves for Pont Clouds of Large Scale Envronments Taash Maeno, Hroa Date and Satosh Kana 3 Graduate School of Informaton Scence and Technology, Hoado Unversty, Japan t_maeno@sdm.ss.st.houda.ac.jp, hdate@ss.st.houda.ac.jp, 3 ana@ss.st.houda.ac.jp Abstract: Recently, wth the progress of laser scannng technology, t has become possble to easly acqure pont clouds of large scale envronments from several scannng platforms, and these pont clouds are used n several felds such as smulaton analyss, cty plannng, and plant management. Vewng the scenes acqured by laser scannng s necessary for checng scanned envronments. However, t s dffcult to understand the scanned envronments only by dsplayng ponts as a renderng prmtve. There are several exstng renderng methods for pont-sampled objects, such as methods usng splats or surface mesh models n computer graphcs feld. However, t s dffcult to acheve an effectve vew of the scenes of large scale envronments wth the exstng renderng methods because the data have extremely non-unform pont densty and spatal dstrbuton and also nclude varous nds of objects wth dfferent scales and shape complexty. In ths paper, n order to realze effectve vews of the scanned large scale envronments, we descrbe a method for generatng renderng models and pont herarches of scanned large scale envronments, as well as a method for LOD renderng usng them. Keywords: Laser Scannng, Pont Cloud, Renderng, Splat, Mesh. Introducton Laser scannng technology has become more and more common, and the development of technology has enabled the easy acquston of pont clouds of large scale envronments from ndoor to outdoor scenes. These pont clouds are used n several felds such as smulaton analyss, cty plannng, and plant management. There are 3 typcal scannng types: TLS (Terrestral Laser Scannng), MMS (Moble Mappng System), and ALS (Arborne Laser Scannng). For example, MMS can acqure pont clouds of large area, such as urban areas, usng a sensor mounted moble vehcle, and pont clouds acqured from MMS are used for smulatng a landscape of a cty, perodc checup of utltes such as roads, tunnels, etc. Vewng the scenes acqured by laser scannng s necessary for checng the scanned envronments. However, t s dffcult to understand the scanned envronments only by dsplayng ponts as a renderng prmtve, whch do not have surface nformaton. There are typcally two types of pont cloud renderng methods, polygon-based renderng and pont-based renderng. A renderng method usng trangular mesh models s one of the most major polygon-based renderng methods. The surface of pont-sampled objects can be reconstructed by generatng mesh models. On the other hand, the pont-based renderng method, as represented by splattng, has a smple data structure and does not need to construct topologcal nformaton because a model s constructed from each pont. Moreover, pont-based renderng can easly sample ponts n sutable densty for a specfc mage resoluton, and t s sutable for LOD (Level of Detal) processng, whch s necessary n case of handlng large scale data sets. However, t s dffcult to acheve an effectve vew for understandng the scanned large scale envronments usng these methods because the data have extremely non-unform pont densty and spatal dstrbuton and also nclude varous nds of objects wth dfferent scales and shape complexty. In ths paper, we descrbe a method for generatng renderng models and pont herarches approprate for effectve vews of scanned large scale envronments and a method for LOD renderng usng them. A renderng model conssts of three types of prmtves,.e. lne segment, quadrlateral splat, and trangular mesh, and t s generated by adaptve selecton of them based on dmensonal analyss of local pont dstrbuton. Pont herarchy s created usng octree structure and quantzaton. The rest of ths paper s organzed as follows. Related wors are descrbed n secton. In secton 3, an overvew of our renderng method s explaned. In secton 4, a method for renderng model generaton s descrbed. Creaton of pont herarchy based on octree s mentoned n secton 5. In secton 6, LOD renderng usng a renderng model and pont herarchy s descrbed. Results and evaluatons are shown n secton 7, and fnally, conclusons and future wors are dscussed n secton 8.. Related Wors The trangular mesh s often generated from pont clouds for renderng applcaton. There are varous studes on surface reconstructon methods from pont clouds [-3]. However, mesh qualty (whether the mesh represents a correct geometry of the objects) may vary dependng on the propertes of the pont clouds. For
example, mesh models cannot be always generated successfully for pont clouds of large scale envronments that have extremely non-unform pont densty and nclude objects of complcated shapes. Moreover, topologcal nformaton s not necessarly requred for renderng applcatons. Pont-based renderng s one of the renderng methods for several geometrc models. In pont-based renderng, models are regarded as a set of ponts, and renderng prmtves are defned at each pont ndvdually. Ponts were frst used as unversal renderng prmtves for renderng geometrc models by Levoy and Whtted [4]. Splattng [5] s one of the pont-based renderng technques. Splattng defnes fnte dss or ellpses n object space nstead of ponts and renders them n mage-space by projectng them onto a screen. Naagawa [6] developed a pont-based renderng applcaton whch can generate spatally nterpolated vrtual realty data called LDAR VR. LDI (Layered Depth Image) [7] s also one of the pont-based renderng methods usng splattng. In ths method, each pxel of a gven screen holds a lst of all color and depth values of the objects that ntersect wth a gven sght lne. In order to generate an mage from several vewponts, LDC (Layered Depth Cube) [8] s developed and conssts of 3 LDI whch are assocated wth 3 axes. Addtonally, LDI tree [9] enables an approprate samplng of a LDI pxel accordng to the poston and the resoluton of the reference mage by constructng herarchcal structure usng an octree structure, whose nodes are assocated wth LDI. One of the problems of pont-based renderng s to generate hole-free renderng on screen. Pfster, et al. [0] proposed the pont-based renderng method usng surface elements as renderng prmtves, called Surfels, n order to close the holes and gaps between sample ponts. Ths method represents an object based on ponts by herarchcal structure usng LDI. Surface splattng [] renders object-space dss or ellpses nstead of ponts for a hole-free renderng n mage-space. Ths method proposes an effectve renderng of pont-sampled objects by texture mappng usng a screen space EWA (Ellptcal Weghted Average) flter. Rusnewcz et al. [] proposed a method for effcent renderng of large scale 3D mesh data usng the mult-resoluton pont renderng technque. For the multresoluton renderng, the data s converted nto a tree structure. The nodes are lad out n breadth-frst order, and durng renderng, the approprate resoluton can be loaded progressvely dependng on the vewpont. Wand et al. [3] descrbed a new out-of-core mult-resoluton data structure for real-tme vsualzaton and edtng of large scale pont clouds. To acheve effcent renderng, mult-resoluton data structure s created by convertng the pont clouds nto an octree structure and creatng the quantzed ponts by herarchcal down samplng at each nner node of the octree. Usually, pont based renderng methods usng ansotropc dss or ellpses as renderng prmtves Input pont cloud Vewpont Parameters Renderng model generaton A Parameters Herarchcal structure generaton A Preprocess Down-sampled ponts far Renderng model (Splat, Lne segment, Mesh) Herarchcal pont cloud Vewpont Orgnal ponts LOD Renderng result Renderng A3 Onlne process close Splat Mesh Lne segment Fgure. Proposed vew-dependent LOD renderng method Pont Cloud {P } lnear planar other Searchng Radus Renderng Prmtves Dmensonal Feature D Pont Classfcaton Prncpal Drecton Lne Segment Splat Mesh A Regon Growng Pont Number PCA {Segment} Segmentaton {Planar Pont} Threshould Renderng A Model Renderng model Other Object Segment Generaton Method(4.3,B) generaton A3 Lnear Object Segment Delaunay Tetrahedralzaton Generaton Method(4.3,A) Lne Segment Generaton Method (4.4,B) Splat Generaton Method (4.4,C) Fgure. Proposed renderng model generaton method requre a normal for each pont. However, t s dffcult to derve a correct normal for each pont from pont clouds of large scale envronments because they have extremely non-unform pont densty and spatal dstrbuton. In our method, several types of prmtves were used n renderng smultaneously to get effectve vews of such envronments, and correct normal for each pont s not requred. 3. Renderng of Scanned Large Scale Envronments usng Adaptve Prmtve Selecton and LOD In the renderng of scanned large scale envronments, usng adaptve prmtves s useful because the envronment ncludes several types of objects, such as pole le objects, buldngs, power lnes, cars, trees, roads, and so on. For example, compared wth polygons or ponts, t s better to use lnear type prmtves (lne segment or cylnder) for renderng power lnes or pole le objects. In addton, LOD renderng of scanned envronments s necessary for effcent renderng because the envronments are vewed from several vewponts. Therefore, the constructon of a renderng model created by adaptve prmtve selecton and LOD technque usng them are proposed n ths paper. In our method, the scanned envronment (scene) s rendered effectvely by vew-dependent LOD usng
herarchcal pont cloud representatons and a renderng model wth several prmtves (lne segment, splat, mesh) as shown n Fg.. When the vewpont s far from the scene, quantzed ponts whch are herarchcally down sampled ponts based on an octree structure are used. When the vewpont s moved closer to the scene, orgnal ponts are rendered. In addton, when the vewpont s moved closer and closer to the scene, a renderng model s rendered, whch s generated by adaptvely selectng renderng prmtves. The renderng model and pont herarches are generated n preprocess n our method as shown n Fg.. 4. Renderng Model Generaton 4. Overvew In renderng model generaton, frst, each pont s classfed nto a lnear object pont, a planar object pont, or an other object pont. Then, a lne segment s selected as a renderng prmtve for lnear object ponts, and a quadrlateral surface (splat) and a trangular mesh are selected as renderng prmtves for planar object ponts as well as other object ponts respectvely. Fgure shows an overvew of the renderng model generaton method. The model s generated n preprocess. Frst, usng PCA (Prncpal Component Analyss) and regon growng, each pont s classfed nto a lnear object pont, a planar object pont, or an other object pont. Next, lnear object segments and other object segments are generated based on the result of pont classfcaton. Fnally, the renderng model s generated from the result of pont classfcaton and segmentaton. 4. Pont Classfcaton (Fg. (A)) Dmensonal analyss of pont clouds usng PCA s commonly used for classfcaton and segmentaton purposes. Vandapel, et al. [4] classfy ponts based on the dmensonal analyss usng PCA. However, they menton that t s dffcult to classfy ponts based on only a result of PCA because the result vares consderably dependng on the type of terran and the sensor. Jerome, et al. [5] classfy ponts by adaptvely tunng a parameter (search range for neghborng ponts) of PCA based on pont dstrbuton and entropy. In our research, we also use PCA for pont classfcaton. Frst, planar regons are recognzed because they can be found a lot n pont clouds of large scale envronments, such as buldngs and roads n urban envronments. In our method, PCA s appled to pont clouds for ntal pont classfcaton, and ponts are reclassfed based on regon growng n order to correctly classfy planar object ponts. Then, lnear objects and other objects can be segmented wth hgh accuracy n subsequent segmentaton process by classfyng planar ponts correctly. PCA s used n order to nvestgate local pont dstrbuton. Varance-covarance matrx M of neghborng ponts of pont s expressed by Eq. (): M * j ( p p )( p p ) T =, () * j j where p s a coordnate value of pont, * s a set of neghborng ponts of pont, p s a barycenter of *. Egenvalues λ, λ, λ 3 (λ λ λ 3 ) of M are derved by egen analyss, and dstrbuton feature values S, S, S 3 are calculated by Eq. (): S λ λ, S = λ λ3, S3 = αλ3 Lnear Object Pont Planar Object Pont Other Object Pont Fgure 3. Pont classfcaton result based on PCA Lnear Object Pont Planar Object Pont Other Object Pont Fgure 4. Pont classfcaton result after reclassfcaton =, () where α s a coeffcent n order to correctly recognze 3D objects from pont clouds of the object surfaces, and we set α=0 expermentally. Dmensonalty feature [5] D s derved by Eq. (3): D = arg d {,,3 } max( Sd ). (3) Accordng to the D, each pont s classfed nto a lnear object pont (D =), a planar object pont (D =), and an other object pont (D =3). Fgure 3 shows a result of pont classfcaton of MMS pont cloud based on PCA. Ponts near the boundary of objects may be msclassfed as other object ponts n pont classfcaton based only on PCA, even f they are actually planar object ponts (Fg. 3). Therefore, by usng regon growng, reclassfcaton of planar object ponts s performed n order to correctly classfy them. Regon growng s done usng the followng condtons: - Seed pont: a pont whch satsfes D = and has maxmum S. - Condton of growng (addng a pont to the regon): the dstance between the pont and the regon boundary pont (ntally, seed pont) s smaller than a threshold, and dstance between the pont and the
plane s also smaller than a threshold. The plane s defned usng the normal of the seed pont (egenvector corresponds to λ 3 ). Dmensonalty feature D of ponts n the resultng regon are newly set to D =. Usng regon growng, msclassfed planar object ponts near the object boundary can be reclassfed correctly, as shown n Fg. 4. p p δ θ e δ d Barycenter Dstance Integraton 4.3 Segmentaton (Fg. (A)) A) Lnear Object Segmentaton Segments of lnear objects, such as utlty poles and power lnes, are generated from pont clouds wthout planar object ponts. Frst, a pont whch satsfes D = and has maxmum S s selected as a seed pont of a segment. Then, for seed pont (segment boundary pont), a pont whch satsfes the condtons ( p p mn p p ) ± e p p, < δ, d > δ θ, (4) s added to the segment n sequence as shown n Fg. 5(a). Where e s an egenvector of pont correspondng to the largest egenvalue, δ θ and δ d are thresholds. In case of thc lnear objects, such as utlty poles, multple parallel lne segments can be generated, and therefore, neghborng segments are ntegrated. As shown n Fg. 5(b), two segments are ntegrated f they have smlar prncple drectons and the shortest dstance between ftted straght lnes s less than a certan threshold. B) Other Object Segmentaton Segments of other object such as vegetaton, cars, etc. are generated from pont clouds wthout planar object ponts and lnear object ponts usng regon growng. A pont wth D =3 s selected as a seed pont, and a pont satsfyng the condton that the dstance from regon boundary pont (ntally, seed pont) and the pont s wthn a certan threshold s added to the regon. 4.4 Renderng Model Generaton (Fg. (A3)) A) Splat Quadrlateral splat s generated for planar object ponts. Accordng to regularty of laser scanned pont clouds, quadrlateral shapes are selected because t s sutable for fllng gaps n object space. Frst, adjacent ponts p p 4 of pont are searched along the prncpal drectons n order to create splats. As shown n Fg. 6(a), a pont satsfyng the condtons ( p p mn p p ) d > p p, < δ, d δ θ, (5) s selected as an adjacent pont of pont related to drecton vector d. By usng ±e, ±e as d n above condtons, four (a) Lnear object segment generaton Fgure 5. Lnear object segment generaton λ e δ d Search range λ 3 e 3 δ θ p λ e p p :Adjacent pont p (a) Adjacent ponts detecton p + p p Fgure 6. Splat generaton (b) Segment ntegraton v p adjacent ponts can be obtaned. Next, two dfference vectors of adjacent ponts v =p p 3, v =p p 4 are generated. Then, corner ponts are represented by the sum of two dfference vectors weghted by half of the dstance between adjacent ponts and pont, as shown n Fg. 6(b). B) Lne Segment For lnear object segments, frst, a lne s created so that the lne passes the barycenter of segment ponts and has an average drecton of egenvectors {e } of segment ponts as a drectonal vector. Then, segment ponts are projected onto the lne, and lne segments are generated connectng projected ponts n sequence. C) Mesh Trangular mesh s generated for other object segments. There have been varous studes on surface reconstructon from pont clouds [-3]. In our mplementaton, a method based on tetrahedralzaton s used []. 5. Herarchcal Pont Cloud Representaton 5. Octree When the vewpont s far from the scene, vew-dependent LOD of pont clouds s used n renderng. LOD s based on quantzed ponts and octree structure. An octree s a herarchcal representaton of gven pont cloud and s adopted for effcent renderng of a scene by ponts. The herarchy s generated by space subdvson. In Fg. 7, a quadtree s llustrated nstead of an octree for smplcty. An octree s a tree data structure whch s used to partton a 3D space by recursvely subdvdng t nto eght sub-spaces. Each node has eght chldren nodes. The root node s assocated wth a mnmum axs-algned boundng cube of the gven pont p 3 p p 4 v v v + p Splat p (b) Splat generaton p v v
clouds. Each chldren node s assocated wth each unformly subdvded cube of the parent node. The leaf node has ponts n the assocated cube. Each node s recursvely subdvded untl the number of ponts stored n t becomes less than a certan number, n max. For example, Fg. 7 shows the case of n max =0. An octree structure enables effcent renderng and determnng LOD as descrbed n a latter secton. 5. Quantzaton LOD technques [,3] are necessary for effcent renderng of large scale scenes. In order to acheve an LOD representaton, we use quantzed ponts. As shown n Fg. 8, frst, we create a quantzaton grd n each nner node of an octree by unformly dvdng ts cube nto quantzaton grd cells. If there are one or more ponts n each quantzaton grd cell, only one representatve pont s selected randomly and s stored n the node. In ths way, unformly down-sampled ponts can be stored n each nner node. 6. LOD Renderng As mentoned n secton 3, herarchcal pont renderng usng octree structure and quantzed ponts s done when the vewpont s far from the scene. When the vewpont s moved closer, a renderng model generated by several prmtves s rendered. Proposed LOD renderng s done as follows. When the vewpont s far from the scene, a depthfrst search of the octree s performed durng renderng. Depth traversal s done untl the node satsfes the condton s/d < δ, and the ponts of the node are used for renderng, where s s the sde length of quantzaton grd cell, d s the dstance from vewpont to barycenter of the ponts of each octree node, and δ s a threshold (Fg. 9). When the vewpont s moved away from the scene, value d becomes large and more down-sampled ponts n upper level nodes are rendered. When the vewpont s moved closer to the scene and the value s/d exceeds the threshold, more detaled ponts n deeper nodes are rendered. In addton, when the vewpont s moved closer and closer, and the value s/d exceeds the threshold, where d s the dstance between the vewpont and the barycenter of the nearest node, a renderng model generated from several prmtves s rendered. As a result, real-tme vew-dependent LOD can be acheved. In current mplementaton, a renderng model whch represents the scene ncludng all objects s used for renderng (local renderng of the model s not done). 7. Results and Evaluatons 7. Test Data Set and Implementaton The data set used n ths research was scanned by MMS n urban area whch ncluded,585,985 ponts. Our proposed method s mplemented on a standard PC wth Intel Core 7.93GHz, 8GB RAM, and GeForce GTX 470 graphcs board usng OpenGL for renderng. Leaf node d Root node Inner node g 0 Fgure 7. Herarchcal pont cloud representaton : Dstance from vew pont to barycenter Vew pont g Fgure 8. Quantzaton s cell : Sde length of the cell n max =0 Cell Quantzaton grd Barycenter of the ponts Number of ponts n each node Fgure 9. Each parameters used for LOD renderng 7. Pont Cloud Renderng Results Fgure 0 shows the renderng results usng our method and other methods. When a vewpont s far from the scene, t can be rendered wth fewer ponts by LOD renderng whle mantanng a certan FPS around 60. Results of pont renderng at dfferent vewponts are shown n Fg. 0(a)-(b). Effcent renderng s acheved usng LOD renderng of ponts. For vewponts close to the scene, the renderng model s used n renderng. The renderng model generated by our method s shown n Fg. 0(c). The number of splats, lne segments, and mesh models are,56,77, 45,868, and 43 (consst of 558,789 trangles) respectvely. Fgures 0(d), (e), (f) show renderng results usng ponts, splats, and mesh respectvely. Compared wth the renderng result usng only ponts (Fg. 0(d)), our result (Fg. 0(c)) gves easer understandng of the scanned envronment, 7 0 7 7 0 0 Quantzed pont Vew frustum Quantzaton grd
Renderng ponts:83,64 (a) Vewpont: far, Prmtve: pont Renderng ponts:,30,539 (b) Vewpont: mddle, Prmtve: pont A B (c) Vewpont: close Prmtve: pont, lne segment, splat, mesh C D (d) Renderng by ponts E F (e) Renderng by splats wthout pont classfcaton Power Lne A C (f) Renderng by mesh wthout pont classfcaton (Ball Pvotng []) E B D F Road (g) Another vew of (c) (h) Another vew of (e) () Another vew of (f) Fgure 0. Result of pont cloud renderng
(a) Renderng by ponts (b) Renderng model (c) Renderng by ponts (d) Renderng model Fgure. Other results of pont cloud renderng especally at the regon near the vewpont, because the gaps at the ground and the vegetatons are flled by splats and meshes. Unnatural vegetatons can be seen n the renderng result usng only splats (Fg. 0(e)) because t s dffcult to reconstruct complex 3D objects usng planar prmtves from coarse ponts. Moreover, n the result, splats are generated at power lnes (Fg. 0(h)). On the other hand, n our results, undesred splats are not generated at the power lnes, because the lne segment s used for renderng them wth the help of pont classfcaton. Renderng result usng only mesh (Fg. 0(f)) also shows unnatural vews at power lnes (Fg. 0()). Moreover, mesh s not generated at low pont densty areas such as the edge of roads (Fg. 0()) and buldng surfaces. In such areas, the vew s mproved by splats (Fg. 0(g)). Fgure shows other renderng results from dfferent vewponts of the scene (Fg. (a), (b)) and renderng results of another data set (Fg. (c), (d)) scanned by TLS. Compared wth the renderng results usng only ponts, the results usng the renderng models gve easer understandng of the scene. However, unnatural vew around the boundary of the buldngs and wndows can be seen n Fg. (b). Therefore, qualty mprovement of the model usng the recognton of object boundares s requred. The processng tme for generatng the renderng model from the MMS data set was 46 [s]. FPS for renderng usng pont herarchy was around 60, and for renderng model was -3. Improvement of a renderng speed whle usng renderng model s one of our future wors. 8. Conclusons and Future Wors In ths paper, we developed a method for renderng model generaton by adaptve selecton of renderng prmtves (pont, lne segment, splat, mesh) n order to support vsual understandng for pont clouds of large scale envronments. Our method s based on the dmensonal analyss of local pont dstrbuton. Frst, each pont s classfed as ether a lnear object pont, a planar object pont, or as an other object pont based on PCA and regon growng. Next, lnear object segments and other object segments are generated based on the results of pont classfcaton. Fnally, a renderng model consstng of a set of lne segments, splats and meshes s generated from the result of pont classfcaton and segmentaton. In addton, herarchcal representaton of pont clouds based on octree structure and quantzaton s descrbed for LOD of pont clouds. We confrm that our method provdes better vewng of scanned large scale envronments compared to smply usng a sngle type of prmtve. In the future, we wll ntroduce blendng technques for splats, other types of prmtves to render a scene more effectvely, and LOD of a renderng model. Acnowledgements The data s provded from TOPCON Corporaton, Kosh Corporaton, and The Japan Socety for Precson Engneerng, Cyber Feld Constructon Technque Research Sectonal Commttee.
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