[{"data":1,"prerenderedAt":555},["ShallowReactive",2],{"method-bosche2010asbuiltdims":3},{"method":4,"reference":52,"equipment":69,"figures":76,"results":77},{"id":5,"label":6,"shortName":7,"title":8,"year":9,"era":10,"cluster":11,"scope":12,"keyIdeaZh":13,"keyIdeaEn":14,"fulltextStatus":15,"publicationStatus":16,"recommendation":17,"constructionRelevance":18,"validationEnvironment":19,"strengths":21,"limitations":27,"sensors":36,"platform":38,"estimator":40,"association":41,"timeModel":40,"deskew":40,"loopClosure":40,"globalOptimization":40,"mapRepresentation":42,"prior":43,"outputGeometry":44,"compute":45,"codeUrl":46,"codeLicense":40,"relatedVersions":47},"bosche2010asbuiltdims","Bosché, 2010","Scan-vs-BIM object recognition and as-built dimensions","Automated recognition of 3D CAD model objects in laser scans and calculation of as-built dimensions for dimensional compliance control in construction",2010,"classic","C11b","downstream_engineering_task","作者改良先前的方法，先以人工選三組以上對應點把工地雷射掃描粗對齊專案 3D CAD 模型，再以新的 ICP 精對齊整個模型，依與各構件表面相符的點數與覆蓋面積判定構件是否被辨識。接著對每個被辨識的構件個別再做 ICP，求得其竣工位姿，並與設計位姿比較，推算柱垂直度與柱間距等尺寸以檢查是否符合容許差。實驗使用加拿大多倫多一座發電廠鋼構廠房施工期間的五次掃描。作者坦承缺乏真值，且位姿偏差與掃描距離相關，結果尚不足以判斷尺寸合規檢查的精度。","Registers site laser scans to the project CAD model with an improved ICP, recognizes objects by matched surface, then refines each object's as-built pose to compute dimensions for tolerance compliance; accuracy could not be established for lack of ground truth.","full_text_reviewed","peer_reviewed_published","background","施工中鋼構廠房（Portland Energy Center 發電廠專案，多倫多）的現場 TLS 掃描，屬真實工地資料；以 AISC 303-05 與 MNL 135-00 為容許差來源示例（Sec. 3.2）。展示掃描誤差如何傳遞到竣工尺寸，但無獨立參考。",[20],"real_construction_site",[22,23,24,25,26],"Model fine registration lowered MSE and increased matched points for all five scans compared with the earlier method (Table 2)","Object fine registration further lowered MSE (13-37 mm2) (Table 4)","Recognition is quasi-automated, robust to clutter and occlusion, and efficient (Sec. 4)","The author states that recall improved for all scans; Table 3 shows higher recall for Scans 2-5 and equal recall for Scan 1 (83%), overall recall 83% vs 80% and precision 93% vs 91%, but lower precision for Scans 4 and 5 (93% vs 94%, 82% vs 84%); gains were small because the manual coarse registrations were already accurate (Sec. 2.4.3, Table 3)","Model fine registration took about 2 min per iteration on CPU for about 650,000 points and about 20,000 facets; without the acceleration it would take about two orders of magnitude longer (Sec. 2.4.2)",[28,29,30,31,32,33,34,35],"Assumes each object's shape already complies with tolerances, reasonable only for prefabricated elements (Sec. 3.1)","No ground truth for as-built poses or dimensions; results not reliable enough to conclude on accuracy for dimensional compliance (Sec. 3.3.1)","Calculated pose deviations correlate with scanner range (r = 0.45; columns 20-80 m from the scanner), possibly from range-dependent scanner error or fewer recognized points (Sec. 3.3.1)","Per-object registration is often ill-conditioned because column ends are occluded (Sec. 3.3.1)","Coarse registration is manual; mesh models lack semantics, so control points were computed manually (Sec. 2.1, 3.3.2)","Recall and precision rely on object presence identified by manual visual inspection of each scan (footnote to Sec. 2.4.3)","The expert manual time estimate (a few hours to one day) is not based on field measurements (footnote to Sec. 3.3.2)","Object poses are refined independently, which may produce clashes between objects (Sec. 5)",[37],"terrestrial laser scanner (Trimble GX 3D per Sec. 1.1.2 and ref. [44])",[39],"static terrestrial (tripod)","not_applicable","closest orthogonal projection of each scan point onto model facets, accelerated with a bounding-volume hierarchy and frustum and back-face culling; ICP-based model fine registration, then per-object fine registration (Sec. 2.2-3.1); point pairs rejected when their distance exceeds tau_D = max(2 sqrt(MSE of previous iteration), 50 mm) or their normals differ by more than 45 deg; iteration stops when the MSE improvement is below 2 mm2 (Sec. 2.2.2)","point cloud with per-point CAD-object labels","3D CAD model (mesh) of the steel structure, 612 objects (Sec. 2.4)","recognized objects, as-built object poses, derived dimensions such as column plumb and inter-column distances (Sec. 3, Tables 5-6)","CPU implementation, hardware not reported; model fine registration of Scan 4 took about 2 min per iteration over 5 iterations (5 to 10 iterations for the other scans), about 10 min in total; processing Scan 4 including model fine registration, object recognition and as-built pose calculation took about 35 min, 30 min of which for the as-built pose calculation (Sec. 2.4.2, 3.3.2)",null,[48],{"relation":49,"title":50,"doi_or_url":51},"preprint","Early version (pre-print) deposited in Heriot-Watt Pure","https:\u002F\u002Fpure.hw.ac.uk\u002Fws\u002Ffiles\u002F784859\u002FAEI_2009.pdf",{"id":5,"kind":53,"shortName":7,"title":8,"authors":54,"year":9,"venue":56,"venueType":57,"publisher":58,"volumeIssuePages":59,"doi":60,"arxivId":46,"url":51,"firstPublicDate":61,"publicationStatus":16,"metadataStatus":62,"fulltextStatus":15,"era":10,"classicReason":63,"codeUrl":46,"cluster":11,"topics":64,"mdpi":65,"verification":66,"label":6,"fulltextRoute":67,"versionRead":68,"addedByCensus":65},"method",[55],"Frédéric Bosché","Advanced Engineering Informatics","journal","Elsevier","24(1), 107-118","10.1016\u002Fj.aei.2009.08.006","2009-09-12","metadata_verified","principle reused: the Scan-vs-BIM registration and point-to-object matching is reused by Bosché and Guenet 2014 (their ref. [31]) and underlies later progress and QA work; it also shows how scan error propagates into as-built dimensions.",[11],false,"corrected","author copy","Heriot-Watt Research Portal deposit labelled 'Early version, also known as pre-print' (dated 11 Aug 2009) read in full; version of record (Advanced Engineering Informatics 24(1) 107-118, ScienceDirect HTML via NTU access) Sec. 2.4-5 and Tables 1-6 compared and found identical in values",[70],{"category":71,"model":72,"canonical":72,"role":73,"dataset":46,"specs":74,"locator":75},"tls_scanner","Trimble GX 3D (identified via ref. [44] cited for the scanner used in this research)","method input","about 12 mm accuracy at 100 m; maximum horizontal and vertical resolution about 60 microrad (about one point per 3 mm at 50 m); scans 1-4 at 582 microrad and scan 5 at 300 microrad (Table 1)","Sec. 1.1.2, ref. [44], Table 1",[],{"totalRows":78,"groupCount":79,"groups":80,"others":534},129,8,[81,245,395,466],{"slug":82,"group":83,"sourceId":5,"sourceLabel":6,"table":84,"selfRows":85,"metrics":86,"seqs":97,"entrants":132,"cells":136,"outcomes":239,"locators":240,"hardware":241,"wordings":242,"notes":243},"bosche2010asbuiltdims-table-5","bosche2010asbuiltdims:Table 5","Table 5",48,[87,92,94],{"label":88,"unit":89,"statistic":90,"alignment":91},"Bottom point Delta XYZ (as-built minus as-designed)","mm","not_reported","control points",{"label":93,"unit":89,"statistic":90,"alignment":91},"Top point Delta XYZ (as-built minus as-designed)",{"label":95,"unit":96,"statistic":90,"alignment":91},"Delta plumb (as-built minus as-designed)","%",[98,102,104,106,108,110,112,114,116,118,120,122,124,126,128,130],{"dataset":99,"sequence":100,"environment":101},"PEC steel structure scans","column 1 (estimated range 82.5 m)","active construction site: steel structure erection, Portland Energy Center powerplant, Toronto",{"dataset":99,"sequence":103,"environment":101},"column 2 (estimated range 74.6 m)",{"dataset":99,"sequence":105,"environment":101},"column 3 (estimated range 66.7 m)",{"dataset":99,"sequence":107,"environment":101},"column 4 (estimated range 58.9 m)",{"dataset":99,"sequence":109,"environment":101},"column 5 (estimated range 51.2 m)",{"dataset":99,"sequence":111,"environment":101},"column 6 (estimated range 43.7 m)",{"dataset":99,"sequence":113,"environment":101},"column 7 (estimated range 36.5 m)",{"dataset":99,"sequence":115,"environment":101},"column 8 (estimated range 28.3 m)",{"dataset":99,"sequence":117,"environment":101},"column 18 (estimated range 21.9 m)",{"dataset":99,"sequence":119,"environment":101},"column 17 (estimated range 31.8 m)",{"dataset":99,"sequence":121,"environment":101},"column 16 (estimated range 39.9 m)",{"dataset":99,"sequence":123,"environment":101},"column 15 (estimated range 48.0 m)",{"dataset":99,"sequence":125,"environment":101},"column 14 (estimated range 56.1 m)",{"dataset":99,"sequence":127,"environment":101},"column 13 (estimated range 64.3 m)",{"dataset":99,"sequence":129,"environment":101},"column 12 (estimated range 72.4 m)",{"dataset":99,"sequence":131,"environment":101},"column 11 (estimated range 80.5 m)",[133],{"name":134,"methodId":5,"linkable":135,"proposed":135,"self":135},"New with object fine registration",true,[137,141,144,147,149,151,153,155,157,159,162,164,166,169,171,173,176,178,180,183,184,186,189,191,193,195,196,197,200,201,202,205,207,209,212,213,215,216,218,220,222,224,226,229,231,233,236,238],[138,138,138,139,140,138,140,140,138],0,10.2,-1,[138,142,138,143,140,138,140,140,138],1,19,[138,145,138,146,140,138,140,140,138],2,0.33,[138,138,142,148,140,138,140,140,138],12.7,[138,142,142,150,140,138,140,140,138],17.8,[138,145,142,152,140,138,140,140,138],0.38,[138,138,145,154,140,138,140,140,138],11.3,[138,142,145,156,140,138,140,140,138],16.9,[138,145,145,158,140,138,140,140,138],0.34,[138,138,160,161,140,138,140,140,138],3,8.2,[138,142,160,163,140,138,140,140,138],12,[138,145,160,165,140,138,140,140,138],0.26,[138,138,167,168,140,138,140,140,138],4,23.3,[138,142,167,170,140,138,140,140,138],5.2,[138,145,167,172,140,138,140,140,138],0.23,[138,138,174,175,140,138,140,140,138],5,16.7,[138,142,174,177,140,138,140,140,138],16,[138,145,174,179,140,138,140,140,138],0.11,[138,138,181,182,140,138,140,140,138],6,16.6,[138,142,181,154,140,138,140,140,138],[138,145,181,185,140,138,140,140,138],0.32,[138,138,187,188,140,138,140,140,138],7,7.7,[138,142,187,190,140,138,140,140,138],2.2,[138,145,187,192,140,138,140,140,138],0.1,[138,138,79,194,140,138,140,140,138],1.8,[138,142,79,142,140,138,140,140,138],[138,145,79,138,140,138,140,140,138],[138,138,198,199,140,138,140,140,138],9,4.9,[138,142,198,187,140,138,140,140,138],[138,145,198,179,140,138,140,140,138],[138,138,203,204,140,138,140,140,138],10,4.2,[138,142,203,206,140,138,140,140,138],16.5,[138,145,203,208,140,138,140,140,138],0.21,[138,138,210,211,140,138,140,140,138],11,11.1,[138,142,210,181,140,138,140,140,138],[138,145,210,214,140,138,140,140,138],0.22,[138,138,163,181,140,138,140,140,138],[138,142,163,217,140,138,140,140,138],4.8,[138,145,163,219,140,138,140,140,138],0.09,[138,138,221,154,140,138,140,140,138],13,[138,142,221,223,140,138,140,140,138],13.4,[138,145,221,225,140,138,140,140,138],0.31,[138,138,227,228,140,138,140,140,138],14,6.8,[138,142,227,230,140,138,140,140,138],16.4,[138,145,227,232,140,138,140,140,138],0.3,[138,138,234,235,140,138,140,140,138],15,5.4,[138,142,234,237,140,138,140,140,138],19.8,[138,145,234,225,140,138,140,140,138],[],[84],[],[],[244],"As-built minus as-designed pose of the 16 exterior columns from Scan 4 only; no ground truth or manual survey available; the Delta Z columns (all 0.0 to 0.1 mm) are omitted",{"slug":246,"group":247,"sourceId":5,"sourceLabel":6,"table":248,"selfRows":249,"metrics":250,"seqs":255,"entrants":300,"cells":302,"outcomes":389,"locators":390,"hardware":391,"wordings":392,"notes":393},"bosche2010asbuiltdims-table-6","bosche2010asbuiltdims:Table 6","Table 6",44,[251,253],{"label":252,"unit":89,"statistic":90,"alignment":91},"Bottom point Delta XYZ of inter-column distance",{"label":254,"unit":89,"statistic":90,"alignment":91},"Top point Delta XYZ of inter-column distance",[256,258,260,262,264,266,268,270,272,274,276,278,280,282,284,286,288,290,292,294,296,298],{"dataset":99,"sequence":257,"environment":101},"columns 2-1",{"dataset":99,"sequence":259,"environment":101},"columns 3-2",{"dataset":99,"sequence":261,"environment":101},"columns 4-3",{"dataset":99,"sequence":263,"environment":101},"columns 5-4",{"dataset":99,"sequence":265,"environment":101},"columns 6-5",{"dataset":99,"sequence":267,"environment":101},"columns 7-6",{"dataset":99,"sequence":269,"environment":101},"columns 8-7",{"dataset":99,"sequence":271,"environment":101},"columns 17-18",{"dataset":99,"sequence":273,"environment":101},"columns 16-17",{"dataset":99,"sequence":275,"environment":101},"columns 15-16",{"dataset":99,"sequence":277,"environment":101},"columns 14-15",{"dataset":99,"sequence":279,"environment":101},"columns 13-14",{"dataset":99,"sequence":281,"environment":101},"columns 12-13",{"dataset":99,"sequence":283,"environment":101},"columns 11-12",{"dataset":99,"sequence":285,"environment":101},"columns 1-11",{"dataset":99,"sequence":287,"environment":101},"columns 2-12",{"dataset":99,"sequence":289,"environment":101},"columns 3-13",{"dataset":99,"sequence":291,"environment":101},"columns 4-14",{"dataset":99,"sequence":293,"environment":101},"columns 5-15",{"dataset":99,"sequence":295,"environment":101},"columns 6-16",{"dataset":99,"sequence":297,"environment":101},"columns 7-17",{"dataset":99,"sequence":299,"environment":101},"columns 8-18",[301],{"name":134,"methodId":5,"linkable":135,"proposed":135,"self":135},[303,305,307,309,311,312,314,316,318,320,322,324,326,328,330,332,334,336,337,339,341,343,345,347,349,351,353,354,356,358,359,361,363,365,367,370,372,375,377,379,381,384,385,387],[138,138,138,304,140,138,140,140,138],-5.2,[138,142,138,306,140,138,140,140,138],-1.3,[138,138,142,308,140,138,140,140,138],1.2,[138,142,142,310,140,138,140,140,138],-0.9,[138,138,145,160,140,138,140,140,138],[138,142,145,313,140,138,140,140,138],-4.9,[138,138,160,315,140,138,140,140,138],30.9,[138,142,160,317,140,138,140,140,138],-6.7,[138,138,167,319,140,138,140,140,138],-25.2,[138,142,167,321,140,138,140,140,138],2.6,[138,138,174,323,140,138,140,140,138],-7.7,[138,142,174,325,140,138,140,140,138],-8.4,[138,138,181,327,140,138,140,140,138],3.6,[138,142,181,329,140,138,140,140,138],1.5,[138,138,187,331,140,138,140,140,138],3.3,[138,142,187,333,140,138,140,140,138],-3.2,[138,138,79,335,140,138,140,140,138],-0.8,[138,142,79,335,140,138,140,140,138],[138,138,198,338,140,138,140,140,138],6.6,[138,142,198,340,140,138,140,140,138],-2.2,[138,138,203,342,140,138,140,140,138],-4.8,[138,142,203,344,140,138,140,140,138],2.4,[138,138,210,346,140,138,140,140,138],5.32,[138,142,210,348,140,138,140,140,138],-9.81,[138,138,163,350,140,138,140,140,138],-4.5,[138,142,163,352,140,138,140,140,138],-3,[138,138,221,306,140,138,140,140,138],[138,142,221,355,140,138,140,140,138],-3.4,[138,138,227,357,140,138,140,140,138],-7.2,[138,142,227,232,140,138,140,140,138],[138,138,234,360,140,138,140,140,138],-3.6,[138,142,234,362,140,138,140,140,138],1.4,[138,138,177,364,140,138,140,140,138],-2.9,[138,142,177,366,140,138,140,140,138],-1.4,[138,138,368,369,140,138,140,140,138],17,-1.8,[138,142,368,371,140,138,140,140,138],1.7,[138,138,373,374,140,138,140,140,138],18,-1.9,[138,142,373,376,140,138,140,140,138],-0.6,[138,138,143,378,140,138,140,140,138],-16.2,[138,142,143,380,140,138,140,140,138],2.1,[138,138,382,383,140,138,140,140,138],20,-13.2,[138,142,382,199,140,138,140,140,138],[138,138,386,331,140,138,140,140,138],21,[138,142,386,388,140,138,140,140,138],-1.1,[],[248],[],[],[394],"Difference between as-built and as-designed distances between structurally connected columns (Scan 4 only); no ground truth",{"slug":396,"group":397,"sourceId":5,"sourceLabel":6,"table":398,"selfRows":163,"metrics":399,"seqs":404,"entrants":417,"cells":422,"outcomes":460,"locators":461,"hardware":462,"wordings":463,"notes":464},"bosche2010asbuiltdims-table-3","bosche2010asbuiltdims:Table 3","Table 3",[400,402],{"label":401,"unit":96,"statistic":90,"alignment":91},"Object recognition recall R%",{"label":403,"unit":96,"statistic":90,"alignment":91},"Object recognition precision P%",[405,407,409,411,413,415],{"dataset":99,"sequence":406,"environment":101},"Scan 1",{"dataset":99,"sequence":408,"environment":101},"Scan 2",{"dataset":99,"sequence":410,"environment":101},"Scan 3",{"dataset":99,"sequence":412,"environment":101},"Scan 4",{"dataset":99,"sequence":414,"environment":101},"Scan 5",{"dataset":99,"sequence":416,"environment":101},"Scan All",[418,420],{"name":419,"methodId":5,"linkable":135,"proposed":135,"self":135},"New",{"name":421,"methodId":46,"linkable":65,"proposed":65,"self":65},"Old (Bosche et al. [11])",[423,425,427,428,430,432,433,435,437,439,440,441,442,444,445,447,449,451,452,453,454,455,456,458],[138,138,138,424,140,138,140,140,138],83,[138,142,138,426,140,138,140,140,138],93,[142,138,138,424,140,138,140,140,138],[142,142,138,429,140,138,140,140,138],90,[138,138,142,431,140,138,140,140,138],77,[138,142,142,426,140,138,140,140,138],[142,138,142,434,140,138,140,140,138],70,[142,142,142,436,140,138,140,140,138],92,[138,138,145,438,140,138,140,140,138],85,[138,142,145,426,140,138,140,140,138],[142,138,145,424,140,138,140,140,138],[142,142,145,436,140,138,140,140,138],[138,138,160,443,140,138,140,140,138],87,[138,142,160,426,140,138,140,140,138],[142,138,160,446,140,138,140,140,138],82,[142,142,160,448,140,138,140,140,138],94,[138,138,167,450,140,138,140,140,138],84,[138,142,167,446,140,138,140,140,138],[142,138,167,446,140,138,140,140,138],[142,142,167,450,140,138,140,140,138],[138,138,174,424,140,138,140,140,138],[138,142,174,426,140,138,140,140,138],[142,138,174,457,140,138,140,140,138],80,[142,142,174,459,140,138,140,140,138],91,[],[398],[],[],[465],"CAD object recognition (same recognition metric, Surf_min about 0.01 m2 for n = 5) after New vs Old registration; objects present in each scan identified by manual visual inspection",{"slug":467,"group":468,"sourceId":5,"sourceLabel":6,"table":469,"selfRows":203,"metrics":470,"seqs":478,"entrants":484,"cells":488,"outcomes":528,"locators":529,"hardware":530,"wordings":531,"notes":532},"bosche2010asbuiltdims-table-2","bosche2010asbuiltdims:Table 2","Table 2",[471,475],{"label":472,"unit":473,"statistic":474,"alignment":91},"MSE of matched point-pair distances","mm2","mean",{"label":476,"unit":477,"statistic":90,"alignment":91},"No. of matches (matched point pairs)","count",[479,480,481,482,483],{"dataset":99,"sequence":406,"environment":101},{"dataset":99,"sequence":408,"environment":101},{"dataset":99,"sequence":410,"environment":101},{"dataset":99,"sequence":412,"environment":101},{"dataset":99,"sequence":414,"environment":101},[485,487],{"name":486,"methodId":5,"linkable":135,"proposed":135,"self":135},"New (model fine registration)",{"name":421,"methodId":46,"linkable":65,"proposed":65,"self":65},[489,491,493,495,497,499,501,503,505,506,508,510,512,514,516,518,520,522,524,526],[138,138,138,490,140,138,140,140,138],183,[138,142,138,492,140,138,140,140,138],359200,[142,138,138,494,140,138,140,140,138],637,[142,142,138,496,140,138,140,140,138],315659,[138,138,142,498,140,138,140,140,138],195,[138,142,142,500,140,138,140,140,138],220547,[142,138,142,502,140,138,140,140,138],894,[142,142,142,504,140,138,140,140,138],183893,[138,138,145,436,140,138,140,140,138],[138,142,145,507,140,138,140,140,138],332862,[142,138,145,509,140,138,140,140,138],2109,[142,142,145,511,140,138,140,140,138],219970,[138,138,160,513,140,138,140,140,138],111,[138,142,160,515,140,138,140,140,138],240327,[142,138,160,517,140,138,140,140,138],321,[142,142,160,519,140,138,140,140,138],218147,[138,138,167,521,140,138,140,140,138],57,[138,142,167,523,140,138,140,140,138],80909,[142,138,167,525,140,138,140,140,138],143,[142,142,167,527,140,138,140,140,138],80170,[],[469],[],[],[533],"Model registration of each site scan to the 3D CAD model (612 objects, 19,478 facets): New = coarse registration plus proposed ICP model fine registration; Old = manual n-point coarse registration only with the earlier matching of Bosche et al. [11]",[535,540,545,550],{"group":536,"slug":537,"sourceLabel":6,"table":538,"selfRows":203,"datasets":539},"bosche2010asbuiltdims:Table 4","bosche2010asbuiltdims-table-4","Table 4",[99],{"group":541,"slug":542,"sourceLabel":6,"table":543,"selfRows":145,"datasets":544},"bosche2010asbuiltdims:Text Sec. 2.4.2","bosche2010asbuiltdims-text-sec-2-4-2","Text Sec. 2.4.2",[99],{"group":546,"slug":547,"sourceLabel":6,"table":548,"selfRows":145,"datasets":549},"bosche2010asbuiltdims:Text Sec. 3.3.1","bosche2010asbuiltdims-text-sec-3-3-1","Text Sec. 3.3.1",[99],{"group":551,"slug":552,"sourceLabel":6,"table":553,"selfRows":142,"datasets":554},"bosche2010asbuiltdims:Text Sec. 3.3.2","bosche2010asbuiltdims-text-sec-3-3-2","Text Sec. 3.3.2",[99],1790510663764]