[{"data":1,"prerenderedAt":863},["ShallowReactive",2],{"method-erasor2021":3},{"method":4,"reference":50,"equipment":71,"figures":78,"results":79},{"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":24,"sensors":33,"platform":35,"estimator":37,"association":38,"timeModel":37,"deskew":39,"loopClosure":37,"globalOptimization":37,"mapRepresentation":40,"prior":41,"outputGeometry":42,"compute":43,"codeUrl":44,"codeLicense":45,"relatedVersions":46},"erasor2021","Lim et al., 2021","ERASOR","ERASOR: Egocentric Ratio of Pseudo Occupancy-Based Dynamic Object Removal for Static 3D Point Cloud Map Building",2021,"recent","C06","map_representation_or_reconstruction","ERASOR 假設都市環境中多數動態物體與地面接觸，以自我中心的極座標區塊計算「偽佔據」（pseudo occupancy，區塊內高度差），比較查詢掃描與地圖子集的比值，找出可能含動態點的區塊；再以區域地面平面擬合（R-GPF）保留地面、剔除其上的動態點。方法不依賴光線追蹤或可視性判斷，但假設位姿已事先最佳化。","ERASOR flags bins whose pseudo-occupancy ratio between query scan and map indicates dynamic objects in contact with the ground, then keeps ground points via region-wise ground plane fitting.","full_text_reviewed","peer_reviewed_published","main_body","無工地測試（SemanticKITTI 序列 00、01、02、05、07 的選定車載片段）。作者明確把都市中建物改建或修復等大尺度變化排除在研究範圍外，只處理車輛與行人等實例層級動態物（Sec. II）；工地結構體本身隨工期變化，正落在這個排除範圍。工地地面高程變化、多樓層、樓板開口與臨時構造物，也可能違反動態物接觸地面以及感興趣體積為地面下 1.0 m 至地面上 3.0 m 的假設（推論）。",[20],"public_benchmark",[22,23],"Visibility-free, avoiding failures of ray-tracing and visibility methods for large nearby objects (Sec. IV-C)","Bin-wise one-shot removal faster than the compared methods on seq 01 (Sec. IV-D, Table III)",[25,26,27,28,29,30,31,32],"Assumes dynamic objects are in contact with the ground (Sec. II-A)","Assumes poses are given after optimization (Sec. II-A)","Found in follow-up benchmark: removes tree trunks and ground near pedestrians because of height sensitivity (dynbench2023 Sec. V-B)","Argued in follow-up work by theory-based analysis (not an experiment): a fixed height threshold would have to be adjusted for survey data captured at varying ground and sensor heights (dufomap2024 Sec. V-B2)","Large-scale changes such as redevelopment or restoration of buildings are declared out of scope; the method targets instance-level dynamic objects (Sec. II)","Lower preservation rate than Removert RM3 on seq 01 (91.487% vs 94.221%), attributed to removal of distant, partially observed vegetation (Sec. IV-C, Table II)","R-GPF reverts a few dynamic points close to the ground, such as wheel contact points (Sec. IV-B, Table I)","Results depend on the ground threshold tau_g; 0.15 was chosen empirically as the preservation-rejection trade-off (Sec. IV-A, Fig. 6)",[34],"3D LiDAR",[36],"vehicle","not_applicable","Egocentric ring-sector bins (R-POD) inside a volume of interest (Lmax 80 m, height from -1.0 m to 3.0 m relative to the ground); pseudo occupancy per bin = max minus min z; the Scan Ratio Test compares query and map pseudo occupancy bin by bin and selects bins whose scan ratio is below 0.2 as potentially dynamic (map bin occupied by an object that is absent in the query); Region-wise Ground Plane Fitting by PCA on the lowest seed points, three iterations, ground threshold tau_g = 0.15 (Sec. II-B to II-E, Sec. IV-A).","not_reported","static point-cloud map","poses assumed already optimized\u002Fcorrected (Sec. II-A)","static point-cloud map with dynamic points removed","0.0732 s per iteration on SemanticKITTI seq 01 versus 1.077 s (OctoMap) and 0.8307 s (Removert); the Peopleremover entry is printed as '1,000' s (Table III). The paper reports no CPU, GPU or other hardware anywhere. The authors state ERASOR is O(n log n) versus O(n) for Removert, yet faster because it removes dynamic points bin-wise in one shot (Sec. IV-D).","https:\u002F\u002Fgithub.com\u002FLimHyungTae\u002FERASOR","not_verified",[47],{"relation":48,"title":49,"doi_or_url":44},"code_release","LimHyungTae\u002FERASOR",{"id":5,"kind":51,"shortName":7,"title":8,"authors":52,"year":9,"venue":56,"venueType":57,"publisher":58,"volumeIssuePages":59,"doi":60,"arxivId":61,"url":62,"firstPublicDate":63,"publicationStatus":16,"metadataStatus":64,"fulltextStatus":15,"era":10,"classicReason":37,"codeUrl":44,"cluster":11,"topics":65,"mdpi":67,"verification":68,"label":6,"fulltextRoute":69,"versionRead":70,"addedByCensus":67},"method",[53,54,55],"Hyungtae Lim","Sungwon Hwang","Hyun Myung","IEEE Robotics and Automation Letters","journal","IEEE","6(2):2272-2279","10.1109\u002Flra.2021.3061363","2103.04316","https:\u002F\u002Farxiv.org\u002Fabs\u002F2103.04316","2021-03-07","metadata_verified",[11,66],"C13",false,"corrected","arXiv","arXiv 2103.04316v1 (2021-03-07; comment states accepted to RA-L with ICRA 2021); IEEE version of record not compared",[72],{"category":73,"model":74,"canonical":74,"role":75,"dataset":76,"specs":39,"locator":77},"lidar","3D LiDAR (model not named in the paper)","dataset sensor","SemanticKITTI","Sec. I, Sec. III-A",[],{"totalRows":80,"groupCount":81,"groups":82,"others":837},75,9,[83,282,506,698],{"slug":84,"group":85,"sourceId":86,"sourceLabel":87,"table":88,"selfRows":89,"metrics":90,"seqs":103,"entrants":117,"cells":128,"outcomes":276,"locators":277,"hardware":278,"wordings":279,"notes":280},"yang2024lifelong-table-ii","yang2024lifelong:Table II","yang2024lifelong","Yang et al., 2024","Table II",18,[91,95,97,99,101,102],{"label":92,"unit":93,"statistic":39,"alignment":94},"PR (preservation rate)","ratio","none",{"label":96,"unit":93,"statistic":39,"alignment":94},"RR (rejection rate)",{"label":98,"unit":93,"statistic":39,"alignment":94},"F1 score",{"label":92,"unit":93,"statistic":100,"alignment":94},"mean",{"label":96,"unit":93,"statistic":100,"alignment":94},{"label":98,"unit":93,"statistic":100,"alignment":94},[104,107,109,111,113,115],{"dataset":76,"sequence":105,"environment":106},"00","urban driving, vehicle-mounted LiDAR",{"dataset":76,"sequence":108,"environment":106},"01",{"dataset":76,"sequence":110,"environment":106},"02",{"dataset":76,"sequence":112,"environment":106},"05",{"dataset":76,"sequence":114,"environment":106},"07",{"dataset":76,"sequence":116,"environment":106},"mean of sequences 00, 01, 02, 05, 07",[118,120,123,126],{"name":7,"methodId":5,"linkable":119,"proposed":67,"self":119},true,{"name":121,"methodId":122,"linkable":119,"proposed":67,"self":67},"Removert","removert2020",{"name":124,"methodId":125,"linkable":67,"proposed":67,"self":67},"Ground-Octomap",null,{"name":127,"methodId":86,"linkable":119,"proposed":119,"self":67},"Ours",[129,133,136,139,141,143,145,147,149,151,154,156,158,160,162,164,166,168,170,172,174,176,178,180,182,184,186,188,190,192,194,196,198,200,202,204,206,208,210,212,214,216,218,220,222,224,225,226,228,231,233,235,237,239,241,243,245,247,249,251,253,256,258,260,262,264,265,267,269,271,273,274],[130,130,130,131,132,130,132,132,130],0,0.9172,-1,[130,134,130,135,132,130,132,132,130],1,0.97,[130,137,130,138,132,130,132,132,130],2,0.9429,[134,130,130,140,132,130,132,132,130],0.9328,[134,134,130,142,132,130,132,132,130],0.7663,[134,137,130,144,132,130,132,132,130],0.8414,[137,130,130,146,132,130,132,132,130],0.7765,[137,134,130,148,132,130,132,132,130],0.9526,[137,137,130,150,132,130,132,132,130],0.8556,[152,130,130,153,132,130,132,132,130],3,0.9471,[152,134,130,155,132,130,132,132,130],0.9712,[152,137,130,157,132,130,132,132,130],0.959,[130,130,134,159,132,130,132,132,130],0.9193,[130,134,134,161,132,130,132,132,130],0.9463,[130,137,134,163,132,130,132,132,130],0.9326,[134,130,134,165,132,130,132,132,130],0.9579,[134,134,134,167,132,130,132,132,130],0.6688,[134,137,134,169,132,130,132,132,130],0.7877,[137,130,134,171,132,130,132,132,130],0.8475,[137,134,134,173,132,130,132,132,130],0.7337,[137,137,134,175,132,130,132,132,130],0.7865,[152,130,134,177,132,130,132,132,130],0.9425,[152,134,134,179,132,130,132,132,130],0.9528,[152,137,134,181,132,130,132,132,130],0.9477,[130,130,137,183,132,130,132,132,130],0.8108,[130,134,137,185,132,130,132,132,130],0.9911,[130,137,137,187,132,130,132,132,130],0.8919,[134,130,137,189,132,130,132,132,130],0.8531,[134,134,137,191,132,130,132,132,130],0.8222,[134,137,137,193,132,130,132,132,130],0.8374,[137,130,137,195,132,130,132,132,130],0.9479,[137,134,137,197,132,130,132,132,130],0.6277,[137,137,137,199,132,130,132,132,130],0.7553,[152,130,137,201,132,130,132,132,130],0.9421,[152,134,137,203,132,130,132,132,130],0.9035,[152,137,137,205,132,130,132,132,130],0.9224,[130,130,152,207,132,130,132,132,130],0.8698,[130,134,152,209,132,130,132,132,130],0.9788,[130,137,152,211,132,130,132,132,130],0.9211,[134,130,152,213,132,130,132,132,130],0.9223,[134,134,152,215,132,130,132,132,130],0.6757,[134,137,152,217,132,130,132,132,130],0.78,[137,130,152,219,132,130,132,132,130],0.6956,[137,134,152,221,132,130,132,132,130],0.939,[137,137,152,223,132,130,132,132,130],0.7992,[152,130,152,177,132,130,132,132,130],[152,134,152,155,132,130,132,132,130],[152,137,152,227,132,130,132,132,130],0.9566,[130,130,229,230,132,130,132,132,130],4,0.92,[130,134,229,232,132,130,132,132,130],0.9833,[130,137,229,234,132,130,132,132,130],0.9506,[134,130,229,236,132,130,132,132,130],0.8482,[134,134,229,238,132,130,132,132,130],0.5758,[134,137,229,240,132,130,132,132,130],0.686,[137,130,229,242,132,130,132,132,130],0.5396,[137,134,229,244,132,130,132,132,130],0.9081,[137,137,229,246,132,130,132,132,130],0.6769,[152,130,229,248,132,130,132,132,130],0.9768,[152,134,229,250,132,130,132,132,130],0.941,[152,137,229,252,132,130,132,132,130],0.9586,[130,152,254,255,132,130,132,132,130],5,0.8874,[130,229,254,257,132,130,132,132,130],0.9739,[130,254,254,259,132,130,132,132,130],0.9278,[134,152,254,261,132,130,132,132,130],0.9029,[134,229,254,263,132,130,132,132,130],0.7017,[134,254,254,175,132,130,132,132,130],[137,152,254,266,132,130,132,132,130],0.7614,[137,229,254,268,132,130,132,132,130],0.8322,[137,254,254,270,132,130,132,132,130],0.7747,[152,152,254,272,132,130,132,132,130],0.9502,[152,229,254,195,132,130,132,132,130],[152,254,254,275,132,130,132,132,130],0.9488,[],[88],[],[],[281],"Dynamic object removal on SemanticKITTI; point-wise labels, moving classes counted as dynamic; sequences and scan ranges follow the ERASOR setup; authors note ERASOR ran at a lower frame rate; baseline execution settings otherwise not stated",{"slug":283,"group":284,"sourceId":5,"sourceLabel":6,"table":88,"selfRows":285,"metrics":286,"seqs":293,"entrants":305,"cells":320,"outcomes":500,"locators":501,"hardware":502,"wordings":503,"notes":504},"erasor2021-table-ii","erasor2021:Table II",15,[287,290,292],{"label":288,"unit":289,"statistic":39,"alignment":94},"Preservation Rate (PR)","%",{"label":291,"unit":289,"statistic":39,"alignment":94},"Rejection Rate (RR)",{"label":98,"unit":93,"statistic":39,"alignment":94},[294,297,299,301,303],{"dataset":76,"sequence":295,"environment":296},"00 (frames 4390-4530)","urban driving (countryside, highway, intersections)",{"dataset":76,"sequence":298,"environment":296},"01 (frames 150-250)",{"dataset":76,"sequence":300,"environment":296},"02 (frames 860-950)",{"dataset":76,"sequence":302,"environment":296},"05 (frames 2350-2670)",{"dataset":76,"sequence":304,"environment":296},"07 (frames 630-820)",[306,309,311,314,316,318],{"name":307,"methodId":308,"linkable":119,"proposed":67,"self":67},"OctoMap - 0.05","hornung2013octomap",{"name":310,"methodId":308,"linkable":119,"proposed":67,"self":67},"OctoMap - 0.2",{"name":312,"methodId":313,"linkable":119,"proposed":67,"self":67},"Peopleremover","schauer2018peopleremover",{"name":315,"methodId":122,"linkable":119,"proposed":67,"self":67},"Removert - RM3",{"name":317,"methodId":122,"linkable":119,"proposed":67,"self":67},"Removert - RM3+RV1",{"name":319,"methodId":5,"linkable":119,"proposed":119,"self":119},"ERASOR (Ours)",[321,323,325,327,329,331,333,335,337,339,341,343,345,347,349,351,353,355,357,359,361,363,365,367,369,371,373,375,377,379,380,382,384,386,388,390,392,394,396,398,400,402,404,406,408,410,412,414,416,418,420,422,424,426,428,430,432,434,436,438,440,442,444,446,448,450,452,454,456,458,460,462,464,466,468,470,472,474,476,478,480,482,484,486,488,490,492,494,496,498],[130,130,130,322,132,130,132,132,130],76.731,[130,134,130,324,132,130,132,132,130],99.124,[130,137,130,326,132,130,132,132,130],0.865,[134,130,130,328,132,130,132,132,130],34.568,[134,134,130,330,132,130,132,132,130],99.979,[134,137,130,332,132,130,132,132,130],0.514,[137,130,130,334,132,130,132,132,130],37.523,[137,134,130,336,132,130,132,132,130],89.116,[137,137,130,338,132,130,132,132,130],0.528,[152,130,130,340,132,130,132,132,130],85.502,[152,134,130,342,132,130,132,132,130],99.354,[152,137,130,344,132,130,132,132,130],0.919,[229,130,130,346,132,130,132,132,130],86.829,[229,134,130,348,132,130,132,132,130],90.617,[229,137,130,350,132,130,132,132,130],0.887,[254,130,130,352,132,130,132,132,130],93.98,[254,134,130,354,132,130,132,132,130],97.081,[254,137,130,356,132,130,132,132,130],0.955,[130,130,134,358,132,130,132,132,130],53.163,[130,134,134,360,132,130,132,132,130],99.663,[130,137,134,362,132,130,132,132,130],0.693,[134,130,134,364,132,130,132,132,130],20.777,[134,134,134,366,132,130,132,132,130],99.863,[134,137,134,368,132,130,132,132,130],0.344,[137,130,134,370,132,130,132,132,130],36.349,[137,134,134,372,132,130,132,132,130],93.116,[137,137,134,374,132,130,132,132,130],0.523,[152,130,134,376,132,130,132,132,130],94.221,[152,134,134,378,132,130,132,132,130],93.608,[152,137,134,221,132,130,132,132,130],[229,130,134,381,132,130,132,132,130],95.815,[229,134,134,383,132,130,132,132,130],57.077,[229,137,134,385,132,130,132,132,130],0.715,[254,130,134,387,132,130,132,132,130],91.487,[254,134,134,389,132,130,132,132,130],95.383,[254,137,134,391,132,130,132,132,130],0.934,[130,130,137,393,132,130,132,132,130],54.112,[130,134,137,395,132,130,132,132,130],98.769,[130,137,137,397,132,130,132,132,130],0.699,[134,130,137,399,132,130,132,132,130],23.746,[134,134,137,401,132,130,132,132,130],99.792,[134,137,137,403,132,130,132,132,130],0.384,[137,130,137,405,132,130,132,132,130],29.037,[137,134,137,407,132,130,132,132,130],94.527,[137,137,137,409,132,130,132,132,130],0.444,[152,130,137,411,132,130,132,132,130],76.319,[152,134,137,413,132,130,132,132,130],96.799,[152,137,137,415,132,130,132,132,130],0.853,[229,130,137,417,132,130,132,132,130],83.293,[229,134,137,419,132,130,132,132,130],88.371,[229,137,137,421,132,130,132,132,130],0.858,[254,130,137,423,132,130,132,132,130],87.731,[254,134,137,425,132,130,132,132,130],97.008,[254,137,137,427,132,130,132,132,130],0.921,[130,130,152,429,132,130,132,132,130],76.341,[130,134,152,431,132,130,132,132,130],96.785,[130,137,152,433,132,130,132,132,130],0.854,[134,130,152,435,132,130,132,132,130],33.904,[134,134,152,437,132,130,132,132,130],99.882,[134,137,152,439,132,130,132,132,130],0.506,[137,130,152,441,132,130,132,132,130],38.495,[137,134,152,443,132,130,132,132,130],90.631,[137,137,152,445,132,130,132,132,130],0.54,[152,130,152,447,132,130,132,132,130],86.9,[152,134,152,449,132,130,132,132,130],87.88,[152,137,152,451,132,130,132,132,130],0.874,[229,130,152,453,132,130,132,132,130],88.17,[229,134,152,455,132,130,132,132,130],79.981,[229,137,152,457,132,130,132,132,130],0.839,[254,130,152,459,132,130,132,132,130],88.73,[254,134,152,461,132,130,132,132,130],98.262,[254,137,152,463,132,130,132,132,130],0.933,[130,130,229,465,132,130,132,132,130],77.838,[130,134,229,467,132,130,132,132,130],96.938,[130,137,229,469,132,130,132,132,130],0.863,[134,130,229,471,132,130,132,132,130],38.183,[134,134,229,473,132,130,132,132,130],99.565,[134,137,229,475,132,130,132,132,130],0.552,[137,130,229,477,132,130,132,132,130],34.772,[137,134,229,479,132,130,132,132,130],91.983,[137,137,229,481,132,130,132,132,130],0.505,[152,130,229,483,132,130,132,132,130],80.689,[152,134,229,485,132,130,132,132,130],98.822,[152,137,229,487,132,130,132,132,130],0.888,[229,130,229,489,132,130,132,132,130],82.038,[229,134,229,491,132,130,132,132,130],95.504,[229,137,229,493,132,130,132,132,130],0.883,[254,130,229,495,132,130,132,132,130],90.624,[254,134,229,497,132,130,132,132,130],99.271,[254,137,229,499,132,130,132,132,130],0.948,[],[88],[],[],[505],"Static-map benchmark on five manually selected SemanticKITTI frame ranges with SuMa poses; PR and RR computed voxel-wise with 0.2 voxel size for all methods; OctoMap run at 0.05 and 0.2 voxel sizes; Removert RM3 = three removal stages, RM3+RV1 adds one revert stage",{"slug":507,"group":508,"sourceId":509,"sourceLabel":510,"table":511,"selfRows":512,"metrics":513,"seqs":520,"entrants":536,"cells":550,"outcomes":692,"locators":693,"hardware":694,"wordings":695,"notes":696},"dufomap2024-table-i","dufomap2024:Table I","dufomap2024","Duberg et al., 2024","Table I",12,[514,516,518],{"label":515,"unit":289,"statistic":39,"alignment":94},"SA (static accuracy, share of static points correctly kept)",{"label":517,"unit":289,"statistic":39,"alignment":94},"DA (dynamic accuracy, share of dynamic points correctly labelled)",{"label":519,"unit":289,"statistic":39,"alignment":94},"AA (associated accuracy, sqrt(SA x DA))",[521,525,528,532],{"dataset":522,"sequence":523,"environment":524},"KITTI (SemanticKITTI labels and poses)","00 small town","small town (HDL-64E)",{"dataset":522,"sequence":526,"environment":527},"01 highway","highway (HDL-64E)",{"dataset":529,"sequence":530,"environment":531},"Argoverse 2","big city","urban big city (two VLP-32C)",{"dataset":533,"sequence":534,"environment":535},"Semi-indoor (self-collected)","semi-indoor","highly structured semi-indoor area, sparse 16-channel LiDAR (VLP-16)",[537,539,541,543,545,548],{"name":538,"methodId":122,"linkable":119,"proposed":67,"self":67},"Removert [8]",{"name":540,"methodId":5,"linkable":119,"proposed":67,"self":119},"ERASOR [9]",{"name":542,"methodId":308,"linkable":119,"proposed":67,"self":67},"OctoMap [16]",{"name":544,"methodId":509,"linkable":119,"proposed":119,"self":67},"DUFOMap (Ours)",{"name":546,"methodId":547,"linkable":119,"proposed":67,"self":67},"Dynablox [17]","dynablox2023",{"name":549,"methodId":509,"linkable":119,"proposed":119,"self":67},"DUFOMap* (Ours, online)",[551,553,555,557,559,561,563,565,567,569,571,573,575,577,579,581,583,585,587,589,591,593,595,597,599,601,603,605,607,609,611,613,615,617,619,621,623,625,627,629,631,633,635,637,639,641,643,645,646,648,650,652,654,656,658,660,662,663,665,667,669,671,673,675,677,679,681,683,685,687,689,691],[130,130,130,552,132,130,132,132,130],99.44,[130,134,130,554,132,130,132,132,130],41.53,[130,137,130,556,132,130,132,132,130],64.26,[130,130,134,558,132,130,132,132,130],97.81,[130,134,134,560,132,130,132,132,130],39.56,[130,137,134,562,132,130,132,132,130],62.2,[130,130,137,564,132,130,132,132,130],98.97,[130,134,137,566,132,130,132,132,130],31.16,[130,137,137,568,132,130,132,132,130],55.53,[130,130,152,570,132,130,132,132,130],99.96,[130,134,152,572,132,130,132,132,130],12.15,[130,137,152,574,132,130,132,132,130],34.85,[134,130,130,576,132,130,132,132,130],66.7,[134,134,130,578,132,130,132,132,130],98.54,[134,137,130,580,132,130,132,132,130],81.07,[134,130,134,582,132,130,132,132,130],98.12,[134,134,134,584,132,130,132,132,130],90.94,[134,137,134,586,132,130,132,132,130],94.46,[134,130,137,588,132,130,132,132,130],77.51,[134,134,137,590,132,130,132,132,130],99.18,[134,137,137,592,132,130,132,132,130],87.68,[134,130,152,594,132,130,132,132,130],94.9,[134,134,152,596,132,130,132,132,130],66.26,[134,137,152,598,132,130,132,132,130],79.3,[137,130,130,600,132,130,132,132,130],68.05,[137,134,130,602,132,130,132,132,130],99.69,[137,137,130,604,132,130,132,132,130],82.37,[137,130,134,606,132,130,132,132,130],55.55,[137,134,134,608,132,130,132,132,130],99.59,[137,137,134,610,132,130,132,132,130],74.38,[137,130,137,612,132,130,132,132,130],69.04,[137,134,137,614,132,130,132,132,130],97.5,[137,137,137,616,132,130,132,132,130],82.04,[137,130,152,618,132,130,132,132,130],88.97,[137,134,152,620,132,130,132,132,130],82.18,[137,137,152,622,132,130,132,132,130],85.51,[152,130,130,624,132,130,132,132,130],97.96,[152,134,130,626,132,130,132,132,130],98.72,[152,137,130,628,132,130,132,132,130],98.34,[152,130,134,630,132,130,132,132,130],98.09,[152,134,134,632,132,130,132,132,130],94.2,[152,137,134,634,132,130,132,132,130],96.12,[152,130,137,636,132,130,132,132,130],96.67,[152,134,137,638,132,130,132,132,130],88.9,[152,137,137,640,132,130,132,132,130],92.7,[152,130,152,642,132,130,132,132,130],99.64,[152,134,152,644,132,130,132,132,130],83,[152,137,152,584,132,130,132,132,130],[229,130,130,647,132,130,132,132,130],96.76,[229,134,130,649,132,130,132,132,130],90.68,[229,137,130,651,132,130,132,132,130],93.67,[229,130,134,653,132,130,132,132,130],96.33,[229,134,134,655,132,130,132,132,130],68.01,[229,137,134,657,132,130,132,132,130],80.94,[229,130,137,659,132,130,132,132,130],96.08,[229,134,137,661,132,130,132,132,130],92.87,[229,137,137,586,132,130,132,132,130],[229,130,152,664,132,130,132,132,130],98.81,[229,134,152,666,132,130,132,132,130],36.49,[229,137,152,668,132,130,132,132,130],60.05,[254,130,130,670,132,130,132,132,130],98.37,[254,134,130,672,132,130,132,132,130],92.37,[254,137,130,674,132,130,132,132,130],95.31,[254,130,134,676,132,130,132,132,130],98.48,[254,134,134,678,132,130,132,132,130],81.34,[254,137,134,680,132,130,132,132,130],89.5,[254,130,137,682,132,130,132,132,130],98.66,[254,134,137,684,132,130,132,132,130],73.98,[254,137,137,686,132,130,132,132,130],85.43,[254,130,152,688,132,130,132,132,130],99.94,[254,134,152,690,132,130,132,132,130],54.76,[254,137,152,684,132,130,132,132,130],[],[511],[],[],[697],"Point-wise dynamic point removal accuracy (%) following the DynamicMap benchmark protocol; Removert, ERASOR, OctoMap and DUFOMap evaluated offline, Dynablox and DUFOMap* online (each scan classified with the map built so far); DUFOMap uses the same parameters for all data (voxel 0.1 m, ds 0.2 m, dp 1), Removert and ERASOR per-dataset optimized parameters; KITTI labels and poses from SemanticKITTI",{"slug":699,"group":700,"sourceId":701,"sourceLabel":702,"table":511,"selfRows":512,"metrics":703,"seqs":710,"entrants":723,"cells":734,"outcomes":831,"locators":832,"hardware":833,"wordings":834,"notes":835},"dynbench2023-table-i","dynbench2023:Table I","dynbench2023","Zhang et al., 2023a",[704,706,708],{"label":705,"unit":289,"statistic":39,"alignment":94},"SA (static accuracy)",{"label":707,"unit":289,"statistic":39,"alignment":94},"DA (dynamic accuracy)",{"label":709,"unit":289,"statistic":39,"alignment":94},"AA (associated accuracy)",[711,715,717,720],{"dataset":712,"sequence":713,"environment":714},"KITTI (SemanticKITTI labels)","sequence 00","small town (SemanticKITTI)",{"dataset":712,"sequence":716,"environment":714},"sequence 05",{"dataset":718,"sequence":530,"environment":719},"Argoverse 2.0","car, urban (two VLP-32C)",{"dataset":721,"sequence":534,"environment":722},"Semi-indoor (authors' custom)","semi-indoor, one sparse VLP-16",[724,726,728,730,732],{"name":725,"methodId":122,"linkable":119,"proposed":67,"self":67},"Removert* [5]",{"name":727,"methodId":5,"linkable":119,"proposed":67,"self":119},"ERASOR* [16]",{"name":729,"methodId":308,"linkable":119,"proposed":67,"self":67},"Octomap [8]",{"name":731,"methodId":701,"linkable":67,"proposed":119,"self":67},"Octomap w G",{"name":733,"methodId":701,"linkable":67,"proposed":119,"self":67},"Octomap w GF",[735,736,737,738,740,742,744,745,746,747,748,749,750,751,752,753,755,757,759,760,761,762,763,764,765,766,767,768,770,772,774,776,778,780,781,782,783,785,787,789,791,793,795,797,799,801,803,805,807,809,811,813,815,817,819,821,823,825,827,829],[130,130,130,552,132,130,132,132,130],[130,134,130,554,132,130,132,132,130],[130,137,130,556,132,130,132,132,130],[130,130,134,739,132,130,132,132,130],99.42,[130,134,134,741,132,130,132,132,130],22.28,[130,137,134,743,132,130,132,132,130],47.06,[130,130,137,564,132,130,132,132,130],[130,134,137,566,132,130,132,132,130],[130,137,137,568,132,130,132,132,130],[130,130,152,570,132,130,132,132,130],[130,134,152,572,132,130,132,132,130],[130,137,152,574,132,130,132,132,130],[134,130,130,576,132,130,132,132,130],[134,134,130,578,132,130,132,132,130],[134,137,130,580,132,130,132,132,130],[134,130,134,754,132,130,132,132,130],69.4,[134,134,134,756,132,130,132,132,130],99.06,[134,137,134,758,132,130,132,132,130],82.92,[134,130,137,588,132,130,132,132,130],[134,134,137,590,132,130,132,132,130],[134,137,137,592,132,130,132,132,130],[134,130,152,594,132,130,132,132,130],[134,134,152,596,132,130,132,132,130],[134,137,152,598,132,130,132,132,130],[137,130,130,600,132,130,132,132,130],[137,134,130,602,132,130,132,132,130],[137,137,130,604,132,130,132,132,130],[137,130,134,769,132,130,132,132,130],66.28,[137,134,134,771,132,130,132,132,130],99.24,[137,137,134,773,132,130,132,132,130],81.1,[137,130,137,775,132,130,132,132,130],65.91,[137,134,137,777,132,130,132,132,130],96.7,[137,137,137,779,132,130,132,132,130],79.84,[137,130,152,618,132,130,132,132,130],[137,134,152,620,132,130,132,132,130],[137,137,152,622,132,130,132,132,130],[152,130,130,784,132,130,132,132,130],85.92,[152,134,130,786,132,130,132,132,130],98.88,[152,137,130,788,132,130,132,132,130],92.17,[152,130,134,790,132,130,132,132,130],86.15,[152,134,134,792,132,130,132,132,130],98.46,[152,137,134,794,132,130,132,132,130],92.1,[152,130,137,796,132,130,132,132,130],76.38,[152,134,137,798,132,130,132,132,130],86.26,[152,137,137,800,132,130,132,132,130],81.17,[152,130,152,802,132,130,132,132,130],94.95,[152,134,152,804,132,130,132,132,130],73.95,[152,137,152,806,132,130,132,132,130],83.8,[229,130,130,808,132,130,132,132,130],93.06,[229,134,130,810,132,130,132,132,130],98.67,[229,137,130,812,132,130,132,132,130],95.83,[229,130,134,814,132,130,132,132,130],93.54,[229,134,134,816,132,130,132,132,130],92.48,[229,137,134,818,132,130,132,132,130],93.01,[229,130,137,820,132,130,132,132,130],82.66,[229,134,137,822,132,130,132,132,130],82.44,[229,137,137,824,132,130,132,132,130],82.55,[229,130,152,826,132,130,132,132,130],96.79,[229,134,152,828,132,130,132,132,130],73.5,[229,137,152,830,132,130,132,132,130],84.34,[],[511],[],[],[836],"Point-wise dynamic point removal accuracy (%): SA static accuracy, DA dynamic accuracy, AA = sqrt(SA x DA); methods marked * are offline and need a prior raw map; 'Octomap w G' adds ground estimation and 'Octomap w GF' also statistical outlier filtering (the benchmark's own extension); poses from dataset files for KITTI and AV2.0, simple NDT SLAM for semi-indoor",[838,843,850,855,859],{"group":839,"slug":840,"sourceLabel":510,"table":841,"selfRows":81,"datasets":842},"dufomap2024:Table III","dufomap2024-table-iii","Table III",[712],{"group":844,"slug":845,"sourceLabel":846,"table":847,"selfRows":229,"datasets":848},"lamm2025:Table IV","lamm2025-table-iv","Wei et al., 2025b","Table IV",[849],"HeLiPR",{"group":851,"slug":852,"sourceLabel":510,"table":88,"selfRows":137,"datasets":853},"dufomap2024:Table II","dufomap2024-table-ii",[854,533],"KITTI",{"group":856,"slug":857,"sourceLabel":702,"table":88,"selfRows":137,"datasets":858},"dynbench2023:Table II","dynbench2023-table-ii",[39],{"group":860,"slug":861,"sourceLabel":6,"table":841,"selfRows":134,"datasets":862},"erasor2021:Table III","erasor2021-table-iii",[76],1790510662647]