[{"data":1,"prerenderedAt":583},["ShallowReactive",2],{"method-yang2021teaser":3},{"method":4,"reference":61,"equipment":80,"figures":102,"results":103},{"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":22,"limitations":29,"sensors":39,"platform":41,"estimator":42,"association":43,"timeModel":44,"deskew":44,"loopClosure":45,"globalOptimization":46,"mapRepresentation":47,"prior":48,"outputGeometry":49,"compute":50,"codeUrl":51,"codeLicense":52,"relatedVersions":53},"yang2021teaser","Yang et al., 2021","TEASER \u002F TEASER++","TEASER: Fast and Certifiable Point Cloud Registration",2021,"recent","C02","registration_component","TEASER 以截斷最小平方（Truncated Least Squares, TLS）成本處理大量錯誤對應，並以旋轉平移不變量的圖論框架將尺度、旋轉與平移分解後依序求解。尺度與平移以自適應投票求解，旋轉以半正定鬆弛（TEASER）或逐步非凸化（TEASER++）求解，並用最大團剔除大量離群對應。TEASER++ 另以 Douglas-Rachford 分裂計算最佳性證書，可用來辨識不可靠的配準結果。在 3DMatch 以 3DSmoothNet 對應點測試時，TEASER++ 平均 0.059 秒，八個場景中除 MIT Lab 外成功率都不低於 RANSAC-10K；經證書篩選（CERT）後成功率更高，但證書計算平均需 238 秒。場景對稱或正確對應少於 3 組時仍會失敗。","Certifiable robust registration via TLS cost, decoupled scale\u002Frotation\u002Ftranslation, max-clique outlier pruning, and GNC plus Douglas-Rachford certification in TEASER++.","full_text_reviewed","peer_reviewed_published","main_body","not_reported（對稱性造成失敗的案例與建物重複結構相關，屬推論）",[20,21],"simulation","public_benchmark",[23,24,25,26,27,28],"robust to more than 99% outliers with known scale on benchmarks, with lower errors than GORE and about one order of magnitude faster (Sec. XI-B)","TEASER++ runs in milliseconds (abstract, Sec. XI-B)","MCIS pruning reduces outlier rates to below 10% (Sec. XI-A)","on 3DMatch TEASER++ success is 83.1% to 98.6% per scene and not below RANSAC-10K except MIT Lab, and certified results reach 88.6% to 99.4% (Table II)","object pose on eight RGB-D scenes with mean FPFH inlier ratio 6.53% gives mean errors 0.066 rad and 0.069 m (Table I)","correspondence-free registration succeeds down to 10% overlap and is more accurate than Go-ICP (Sec. XI-C)",[30,31,32,33,34,35,36,37,38],"fails on scenes with symmetric keypoint distributions, e.g., a 180-degree wrong solution in 3DMatch Hotel 3 (Fig. 11)","cannot succeed when fewer than 3 inliers exist, which is not uncommon with current descriptors (Sec. XI-E)","adversarial outliers make outlier rejection ill-posed (Sec. IX, estimation contract)","with unknown scale TEASER and TEASER++ fail at 90% outliers (Sec. XI-B)","the GNC rotation solver alone fails above about 80% outliers, so TEASER++ depends on MCIS pruning (Sec. XI-A, X)","the correspondence-free mode needs quadratically many hypotheses, is dominated by max-clique and scale estimation time and fails below 10% overlap (Sec. XI-C)","TEASER with the full SDP is impractical for real time (Sec. XI-B)","RANSAC slightly better on the Lab scene of 3DMatch (Sec. XI)","running the certification (TEASER++ CERT) on 3DMatch required more than 200 s on average, although most instances were certified within 100 s (Sec. XI)",[40],"[\"RGB-D (3DMatch scans in experiments)\", \"RGB-D (large-scale hierarchical multi-view RGB-D object dataset [36], object pose tests)\", \"sensor-agnostic 3D correspondences\"]",[],"truncated least squares; adaptive voting for scale and translation; max-clique pruning on invariant measurements; SDP relaxation (TEASER) or GNC with Douglas-Rachford certification (TEASER++) for rotation (abstract, Sec. X)","putative correspondences from FPFH for object pose (Sec. XI-D) or 3DSmoothNet descriptors with nearest-neighbour matching on the 5,000 provided keypoints per 3DMatch scan (Sec. XI-E); all-to-all hypotheses (|A| x |B| about 10^4 for 100-point clouds) in the correspondence-free test (Sec. XI-C)","not_applicable","authors suggest certified registration for loop-closure validation in SLAM (Sec. XI)","certifiable global optimality of the rotation subproblem (abstract)","3D point correspondences","none (no initial guess)","scale, rotation, translation with optimality certificate","TEASER in MATLAB with cvx for the SDP is impractical for real time; TEASER++ in C++ (Eigen, OpenMP, parallel max clique) runs under 10 ms with known scale and under 30 ms with unknown scale for N = 100 on a laptop with an i7-8850H CPU and 32 GB RAM (Sec. XI, XI-B); about 2 s for 10,000 correspondences at 95% outliers (App. S); on 3DMatch, 0.059 s average vs 0.008 s for RANSAC-1K and 0.074 s for RANSAC-10K on a Xeon Platinum 8259CL at 2.50 GHz with 12 threads (Table II); certification averages 238.136 s on 3DMatch, 24 DRS iterations of 50 ms each in the synthetic test, and about 1200 s for the full SDP with K = 100 in MOSEK (Sec. VIII-C, XI-A, Table II)","https:\u002F\u002Fgithub.com\u002FMIT-SPARK\u002FTEASER-plusplus","MIT (LICENSE file checked)",[54,58],{"relation":55,"title":56,"doi_or_url":57},"preprint","arXiv:2001.07715 (v1 2020-01-21; v2 2020-10-17)","https:\u002F\u002Farxiv.org\u002Fabs\u002F2001.07715",{"relation":59,"title":60,"doi_or_url":51},"code_release","TEASER-plusplus",{"id":5,"kind":62,"shortName":7,"title":8,"authors":63,"year":9,"venue":67,"venueType":68,"publisher":69,"volumeIssuePages":70,"doi":71,"arxivId":72,"url":57,"firstPublicDate":73,"publicationStatus":16,"metadataStatus":74,"fulltextStatus":15,"era":10,"classicReason":44,"codeUrl":51,"cluster":11,"topics":75,"mdpi":76,"verification":77,"label":6,"fulltextRoute":78,"versionRead":79,"addedByCensus":76},"method",[64,65,66],"Heng Yang","Jingnan Shi","Luca Carlone","IEEE Transactions on Robotics","journal","IEEE","37(2):314-333","10.1109\u002Ftro.2020.3033695","2001.07715","2020-01-21","metadata_verified",[11],false,"corrected","arXiv","arXiv 2001.07715v2 (17 Oct 2020), accepted version for IEEE T-RO, 44 pages including appendices A to W; T-RO version of record not compared",[81,88,92,98],{"category":82,"model":83,"canonical":83,"role":84,"dataset":85,"specs":86,"locator":87},"compute","i7-8850H CPU, 32GB RAM (laptop)","compute for runtime",null,"all tests except 3DMatch","Sec. XI (implementation details)",{"category":82,"model":89,"canonical":89,"role":84,"dataset":85,"specs":90,"locator":91},"Xeon Platinum 8259CL CPU at 2.50GHz (server)","server CPU; 12 threads allocated per algorithm; used for the 3DMatch scan-matching tests","Sec. XI-E",{"category":93,"model":94,"canonical":85,"role":95,"dataset":96,"specs":97,"locator":91},"rgbd","not_reported","dataset sensor","3DMatch","RGB-D scans of 62 indoor scenes (54 train, 8 test); 5,000 keypoints per scan provided",{"category":93,"model":94,"canonical":85,"role":95,"dataset":99,"specs":100,"locator":101},"large-scale hierarchical multi-view RGB-D object dataset [36]","large-scale RGB-D point cloud scenes with object labels","Sec. XI-D, App. T",[],{"totalRows":104,"groupCount":105,"groups":106,"others":543},84,11,[107,210,331,496],{"slug":108,"group":109,"sourceId":5,"sourceLabel":6,"table":110,"selfRows":111,"metrics":112,"seqs":123,"entrants":141,"cells":145,"outcomes":202,"locators":203,"hardware":205,"wordings":206,"notes":207},"yang2021teaser-text-app-t","yang2021teaser:Text App. T","Text App. T",24,[113,117,120],{"label":114,"unit":115,"statistic":94,"alignment":116},"Rotation error","rad","none",{"label":118,"unit":119,"statistic":94,"alignment":116},"Translation error","m",{"label":121,"unit":122,"statistic":94,"alignment":116},"Inlier ratio","%",[124,127,129,131,133,135,137,139],{"dataset":99,"sequence":125,"environment":126},"scene-1","indoor tabletop scenes",{"dataset":99,"sequence":128,"environment":126},"scene-2",{"dataset":99,"sequence":130,"environment":126},"scene-4",{"dataset":99,"sequence":132,"environment":126},"scene-5",{"dataset":99,"sequence":134,"environment":126},"scene-7",{"dataset":99,"sequence":136,"environment":126},"scene-9",{"dataset":99,"sequence":138,"environment":126},"scene-11",{"dataset":99,"sequence":140,"environment":126},"scene-13",[142],{"name":143,"methodId":5,"linkable":144,"proposed":144,"self":144},"TEASER",true,[146,150,153,156,158,160,162,164,166,168,171,173,175,178,180,182,185,187,189,192,194,196,198,200],[147,147,147,148,149,147,149,149,147],0,0.066,-1,[147,151,147,152,149,147,149,149,151],1,0.09,[147,154,147,155,149,147,149,149,151],2,16.91,[147,147,151,157,149,147,149,149,151],0.12,[147,151,151,159,149,147,149,149,151],0.052,[147,154,151,161,149,147,149,149,151],4.55,[147,147,154,163,149,147,149,149,151],0.042,[147,151,154,165,149,147,149,149,151],0.051,[147,154,154,167,149,147,149,149,151],4.56,[147,147,169,170,149,147,149,149,151],3,0.146,[147,151,169,172,149,147,149,149,151],0.176,[147,154,169,174,149,147,149,149,151],2.63,[147,147,176,177,149,147,149,149,151],4,0.058,[147,151,176,179,149,147,149,149,151],0.097,[147,154,176,181,149,147,149,149,151],3.13,[147,147,183,184,149,147,149,149,151],5,0.036,[147,151,183,186,149,147,149,149,151],0.011,[147,154,183,188,149,147,149,149,151],8.29,[147,147,190,191,149,147,149,149,151],6,0.028,[147,151,190,193,149,147,149,149,151],0.016,[147,154,190,195,149,147,149,149,151],6.97,[147,147,197,184,149,147,149,149,151],7,[147,151,197,199,149,147,149,149,151],0.064,[147,154,197,201,149,147,149,149,151],5.23,[],[204],"App. T, Fig. 18",[],[],[208,209],"Per-scene values listed under the Fig. 18 panels in App. T for the Table I experiment; units are not printed per scene (Table I uses rad and m); per-scene object label (cereal box or cap) not transcribed","Same setting as other App. T rows",{"slug":211,"group":212,"sourceId":5,"sourceLabel":6,"table":213,"selfRows":214,"metrics":215,"seqs":222,"entrants":242,"cells":252,"outcomes":323,"locators":324,"hardware":325,"wordings":327,"notes":328},"yang2021teaser-table-ii","yang2021teaser:Table II","Table II",18,[216,218],{"label":217,"unit":122,"statistic":94,"alignment":116},"percentage of correct registration results",{"label":219,"unit":220,"statistic":221,"alignment":116},"Avg. Runtime [s]","s","mean",[223,226,228,230,232,234,236,238,240],{"dataset":96,"sequence":224,"environment":225},"Kitchen","indoor RGB-D scans",{"dataset":96,"sequence":227,"environment":225},"Home 1",{"dataset":96,"sequence":229,"environment":225},"Home 2",{"dataset":96,"sequence":231,"environment":225},"Hotel 1",{"dataset":96,"sequence":233,"environment":225},"Hotel 2",{"dataset":96,"sequence":235,"environment":225},"Hotel 3",{"dataset":96,"sequence":237,"environment":225},"Study",{"dataset":96,"sequence":239,"environment":225},"MIT Lab",{"dataset":96,"sequence":241,"environment":225},"all 8 test scenes",[243,246,248,250],{"name":244,"methodId":245,"linkable":144,"proposed":76,"self":76},"RANSAC-1K","fischler1981ransac",{"name":247,"methodId":245,"linkable":144,"proposed":76,"self":76},"RANSAC-10K",{"name":249,"methodId":5,"linkable":144,"proposed":144,"self":144},"TEASER++",{"name":251,"methodId":5,"linkable":144,"proposed":144,"self":144},"TEASER++ (CERT)",[253,255,257,259,261,263,265,267,269,272,274,276,278,280,282,284,286,288,290,292,294,295,297,299,300,302,304,306,308,310,312,314,316,317,319,321],[147,147,147,254,149,147,149,149,147],91.3,[147,147,151,256,149,147,149,149,151],89.1,[147,147,154,258,149,147,149,149,151],74.5,[147,147,169,260,149,147,149,149,151],94.2,[147,147,176,262,149,147,149,149,151],84.6,[147,147,183,264,149,147,149,149,151],90.7,[147,147,190,266,149,147,149,149,151],86.3,[147,147,197,268,149,147,149,149,151],81.8,[147,151,270,271,149,147,147,149,151],8,0.008,[151,147,147,273,149,147,149,149,151],97.2,[151,147,151,275,149,147,149,149,151],92.3,[151,147,154,277,149,147,149,149,151],79.3,[151,147,169,279,149,147,149,149,151],96.5,[151,147,176,281,149,147,149,149,151],86.5,[151,147,183,283,149,147,149,149,151],94.4,[151,147,190,285,149,147,149,149,151],90.4,[151,147,197,287,149,147,149,149,151],85.7,[151,151,270,289,149,147,147,149,151],0.074,[154,147,147,291,149,147,149,149,151],98.6,[154,147,151,293,149,147,149,149,151],92.9,[154,147,154,281,149,147,149,149,151],[154,147,169,296,149,147,149,149,151],97.8,[154,147,176,298,149,147,149,149,151],89.4,[154,147,183,283,149,147,149,149,151],[154,147,190,301,149,147,149,149,151],91.1,[154,147,197,303,149,147,149,149,151],83.1,[154,151,270,305,149,147,147,149,151],0.059,[169,147,147,307,149,147,149,149,151],99.4,[169,147,151,309,149,147,149,149,151],94.1,[169,147,154,311,149,147,149,149,151],88.7,[169,147,169,313,149,147,149,149,151],98.2,[169,147,176,315,149,147,149,149,151],91.9,[169,147,183,283,149,147,149,149,151],[169,147,190,318,149,147,149,149,151],94.3,[169,147,197,320,149,147,149,149,151],88.6,[169,151,270,322,149,147,147,149,151],238.136,[],[213],[326],"Xeon Platinum 8259CL at 2.50 GHz, 12 threads",[],[329,330],"3DMatch test scenes, 3DSmoothNet correspondences, success = rotation error \u003C 10 deg and translation error \u003C 30 cm; beta = 5 cm; CERT = subset certified with sub-optimality gap 3%; Xeon Platinum 8259CL, 12 threads","Same setting as other Table II rows",{"slug":332,"group":333,"sourceId":334,"sourceLabel":335,"table":213,"selfRows":336,"metrics":337,"seqs":347,"entrants":364,"cells":374,"outcomes":490,"locators":491,"hardware":492,"wordings":493,"notes":494},"lamp2-2022-table-ii","lamp2_2022:Table II","lamp2_2022","Chang et al., 2022",16,[338,340,342,344],{"label":339,"unit":122,"statistic":94,"alignment":44},"recall of correct loop closures passing SAC and ICP",{"label":341,"unit":122,"statistic":94,"alignment":44},"false-positive rate of false loop closures passing SAC and ICP",{"label":343,"unit":119,"statistic":221,"alignment":44},"mean translation error of accepted correct loop closures",{"label":345,"unit":346,"statistic":221,"alignment":44},"mean rotation error of accepted correct loop closures","deg",[348,352,356,360],{"dataset":349,"sequence":350,"environment":351},"CoSTAR multi-robot dataset: Tunnel","Tunnel","NIOSH Safety Research Coal Mine, Pittsburgh (narrow, mostly featureless tunnels)",{"dataset":353,"sequence":354,"environment":355},"CoSTAR multi-robot dataset: Urban","Urban","Satsop abandoned nuclear power plant, Elma (two floors, open areas, small rooms, stairs)",{"dataset":357,"sequence":358,"environment":359},"CoSTAR multi-robot dataset: Final","Final","DARPA SubT Finals course, Louisville Mega Cavern (tunnel, cave and urban-like)",{"dataset":361,"sequence":362,"environment":363},"CoSTAR multi-robot dataset: KU","KU","Kentucky Underground Storage limestone mine, Wilmore (10-20 m wide tunnels)",[365,367,369,371],{"name":366,"methodId":85,"linkable":76,"proposed":76,"self":76},"GT initialization (oracle)",{"name":368,"methodId":85,"linkable":76,"proposed":76,"self":76},"OdomRot [8] initialization (LAMP 1.0)",{"name":370,"methodId":5,"linkable":144,"proposed":144,"self":144},"TEASER++ initialization + GICP",{"name":372,"methodId":373,"linkable":144,"proposed":144,"self":76},"SAC-IA initialization + GICP","rusu2009fpfh",[375,377,379,381,383,385,387,388,390,392,394,396,398,400,402,404,406,407,409,411,413,415,417,419,420,421,423,424,425,427,428,430,431,432,434,436,438,440,442,444,446,448,450,452,453,455,457,459,460,461,463,465,467,469,471,473,474,476,478,480,482,484,486,488],[147,147,147,376,149,147,149,149,147],90.8,[151,147,147,378,149,147,149,149,147],93.9,[154,147,147,380,149,147,149,149,147],76.6,[169,147,147,382,149,147,149,149,147],81.9,[147,147,151,384,149,147,149,149,147],90.5,[151,147,151,386,149,147,149,149,147],78.2,[154,147,151,386,149,147,149,149,147],[169,147,151,389,149,147,149,149,147],79.6,[147,147,154,391,149,147,149,149,147],89.2,[151,147,154,393,149,147,149,149,147],83.4,[154,147,154,395,149,147,149,149,147],68.4,[169,147,154,397,149,147,149,149,147],57,[147,147,169,399,149,147,149,149,147],29,[151,147,169,401,149,147,149,149,147],11.3,[154,147,169,403,149,147,149,149,147],18.5,[169,147,169,405,149,147,149,149,147],17.7,[147,151,147,154,149,147,149,149,147],[151,151,147,408,149,147,149,149,147],2.4,[154,151,147,410,149,147,149,149,147],1.2,[169,151,147,412,149,147,149,149,147],1.4,[147,151,151,414,149,147,149,149,147],0.8,[151,151,151,416,149,147,149,149,147],1.6,[154,151,151,418,149,147,149,149,147],0.6,[169,151,151,151,149,147,149,149,147],[147,151,154,410,149,147,149,149,147],[151,151,154,422,149,147,149,149,147],7.4,[154,151,154,418,149,147,149,149,147],[169,151,154,151,149,147,149,149,147],[147,151,169,426,149,147,149,149,147],0.4,[151,151,169,147,149,147,149,149,147],[154,151,169,429,149,147,149,149,147],0.2,[169,151,169,147,149,147,149,149,147],[147,154,147,152,149,147,149,149,147],[151,154,147,433,149,147,149,149,147],0.86,[154,154,147,435,149,147,149,149,147],0.71,[169,154,147,437,149,147,149,149,147],0.67,[147,154,151,439,149,147,149,149,147],0.44,[151,154,151,441,149,147,149,149,147],1.89,[154,154,151,443,149,147,149,149,147],0.38,[169,154,151,445,149,147,149,149,147],0.52,[147,154,154,447,149,147,149,149,147],0.06,[151,154,154,449,149,147,149,149,147],1.65,[154,154,154,451,149,147,149,149,147],0.32,[169,154,154,418,149,147,149,149,147],[147,154,169,454,149,147,149,149,147],0.26,[151,154,169,456,149,147,149,149,147],0.82,[154,154,169,458,149,147,149,149,147],0.29,[169,154,169,458,149,147,149,149,147],[147,169,147,433,149,147,149,149,147],[151,169,147,462,149,147,149,149,147],8.02,[154,169,147,464,149,147,149,149,147],10.82,[169,169,147,466,149,147,149,149,147],9.63,[147,169,151,468,149,147,149,149,147],1.47,[151,169,151,470,149,147,149,149,147],1.98,[154,169,151,472,149,147,149,149,147],1.36,[169,169,151,416,149,147,149,149,147],[147,169,154,475,149,147,149,149,147],1.11,[151,169,154,477,149,147,149,149,147],6.43,[154,169,154,479,149,147,149,149,147],2.39,[169,169,154,481,149,147,149,149,147],2.99,[147,169,169,483,149,147,149,149,147],0.96,[151,169,169,485,149,147,149,149,147],1.34,[154,169,169,487,149,147,149,149,147],1.45,[169,169,169,489,149,147,149,149,147],1.48,[],[213],[],[],[495],"Loop-closure relative pose estimation with different ICP initializations on ground-truth and false loop-closure sets; SAC cumulative error threshold 32 m (500 iterations), ICP threshold 0.9 m (200 iterations); errors computed on correct loop closures that passed SAC and ICP",{"slug":497,"group":498,"sourceId":5,"sourceLabel":6,"table":499,"selfRows":270,"metrics":500,"seqs":515,"entrants":518,"cells":520,"outcomes":536,"locators":537,"hardware":538,"wordings":539,"notes":540},"yang2021teaser-table-i","yang2021teaser:Table I","Table I",[501,503,505,507,508,511,512,514],{"label":502,"unit":115,"statistic":221,"alignment":116},"Rotation error [rad]",{"label":502,"unit":115,"statistic":504,"alignment":116},"std",{"label":506,"unit":119,"statistic":221,"alignment":116},"Translation error [m]",{"label":506,"unit":119,"statistic":504,"alignment":116},{"label":509,"unit":510,"statistic":221,"alignment":116},"# of FPFH correspondences","correspondences",{"label":509,"unit":510,"statistic":504,"alignment":116},{"label":513,"unit":122,"statistic":221,"alignment":116},"FPFH inlier ratio [%]",{"label":513,"unit":122,"statistic":504,"alignment":116},[516],{"dataset":99,"sequence":517,"environment":126},"eight scenes",[519],{"name":143,"methodId":5,"linkable":144,"proposed":144,"self":144},[521,522,524,526,528,530,532,534],[147,147,147,148,149,147,149,149,147],[147,151,147,523,149,147,149,149,151],0.043,[147,154,147,525,149,147,149,149,151],0.069,[147,169,147,527,149,147,149,149,151],0.053,[147,176,147,529,149,147,149,149,151],525,[147,183,147,531,149,147,149,149,151],161,[147,190,147,533,149,147,149,149,151],6.53,[147,197,147,535,149,147,149,149,151],4.59,[],[499],[],[],[541,542],"Object pose estimation on eight scenes of the UW RGB-D dataset [36]: object (cereal box or cap) cut from the scene, scene randomly transformed, FPFH correspondences, both downsampled so the object has 2,000 points; mean and SD over the eight scenes","Same setting as other Table I rows",[544,551,556,562,568,573,578],{"group":545,"slug":546,"sourceLabel":547,"table":548,"selfRows":190,"datasets":549},"lim2024quatropp:Table 6","lim2024quatropp-table-6","Lim et al., 2024","Table 6",[550],"KITTI",{"group":552,"slug":553,"sourceLabel":554,"table":499,"selfRows":169,"datasets":555},"lim2025kissmatcher:Table I","lim2025kissmatcher-table-i","Lim et al., 2025",[550],{"group":557,"slug":558,"sourceLabel":6,"table":559,"selfRows":169,"datasets":560},"yang2021teaser:Text App. S","yang2021teaser-text-app-s","Text App. S",[561],"Stanford Bunny (synthetic correspondences)",{"group":563,"slug":564,"sourceLabel":6,"table":565,"selfRows":154,"datasets":566},"yang2021teaser:Text Sec. XI-A","yang2021teaser-text-sec-xi-a","Text Sec. XI-A",[567],"Stanford Bunny (synthetic TIMs)",{"group":569,"slug":570,"sourceLabel":6,"table":571,"selfRows":154,"datasets":572},"yang2021teaser:Text Sec. XI-B","yang2021teaser-text-sec-xi-b","Text Sec. XI-B",[561],{"group":574,"slug":575,"sourceLabel":6,"table":576,"selfRows":151,"datasets":577},"yang2021teaser:Text Sec. VIII-C","yang2021teaser-text-sec-viii-c","Text Sec. VIII-C",[44],{"group":579,"slug":580,"sourceLabel":6,"table":581,"selfRows":151,"datasets":582},"yang2021teaser:Text Sec. XI-E","yang2021teaser-text-sec-xi-e","Text Sec. XI-E",[96],1790510665998]