[{"data":1,"prerenderedAt":871},["ShallowReactive",2],{"method-removert2020":3},{"method":4,"reference":52,"equipment":72,"figures":85,"results":86},{"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":34,"platform":36,"estimator":38,"association":39,"timeModel":38,"deskew":40,"loopClosure":38,"globalOptimization":38,"mapRepresentation":41,"prior":42,"outputGeometry":43,"compute":44,"codeUrl":45,"codeLicense":46,"relatedVersions":47},"removert2020","Kim & Kim, 2020","Removert","Remove, then Revert: Static Point cloud Map Construction using Multiresolution Range Images",2020,"recent","C06","map_representation_or_reconstruction","Removert 以多解析度距離影像（range image）比較查詢掃描與含動態點的累積地圖：先保守地只保留確定的靜態點，再逐步放大查詢與地圖的關聯視窗，把被誤刪的靜態點「回復」（revert），藉此隱式補償位姿估計與配準誤差。方法離線處理，輸入為任一光達里程計或 SLAM 輸出的掃描與位姿。","Removert removes dynamic points by multi-resolution range-image comparison between scans and the accumulated map, then reverts falsely removed static points by enlarging the association window to tolerate pose errors.","full_text_reviewed","peer_reviewed_published","main_body","not_reported；被 LT-mapper 用於高動態點移除與變化偵測（ltmapper2022 Sec. V-A1）。",[20],"public_benchmark",[22,23,24,25,26],"Tolerates imperfect pose estimation via multiresolution revert step, shown with SuMa poses instead of ground truth (Sec. IV-A)","Authors report competing with or exceeding human labels in ambiguous regions on KITTI, e.g. tree leaves left unlabeled in SemanticKITTI (Sec. IV-B, Fig. 6)","Needs only point coordinates, without normals, incidence angles or region growing (Sec. III-D)","Worked qualitatively for LiDARs with 27 deg (KITTI) and 45 deg (MulRan) vertical FOV (Sec. IV-B, Fig. 7)","By-product: self-labelled dynamic objects per scan by nearest-point checks against the static map (Sec. IV-C, Fig. 10)",[28,29,30,31,32,33],"Offline only: the paper excludes online operation and does not report speed (Sec. II-A)","Authors' stated limitation: the map range image keeps only the nearest visible point per pixel, so dynamic points behind static structures such as a concrete median barrier are not removed (Sec. IV-D, Fig. 11)","Quantitative evaluation limited to TP, FP and FN point counts on one 100 m KITTI 03 segment, with no numeric comparison against other removal methods (Sec. IV-B, Fig. 9)","Too coarse a resolution wrongly recovers points near the ground, e.g. under cars (Fig. 3 caption)","Non-commercial license (README)","Found in follow-up benchmark: under point-wise evaluation Removert retained almost all static points but labelled few dynamic points; misses are linked to occlusion behind true dynamic points (dynbench2023 Sec. V-A, Table I)",[35],"3D LiDAR",[37],"vehicle (KITTI 01, 03, 08, 09 with SemanticKITTI labels; MulRan KAIST 02 qualitative)","not_applicable","visibility check in range images: the map (per query frame, using its SE(3) pose) and the query scan are projected to fixed-resolution range images keeping the minimum range per pixel; a map point is marked dynamic when the query-minus-map range difference exceeds a range-adaptive threshold (tau_D times range); batch voting over N randomly ordered scans gives a staticity score (alpha_SM = 0.3, alpha_DM = -0.7, tau_S = -0.1); removal at the finest resolution (vertical FOV \u002F number of rays, 0.4 deg on KITTI) for three steps, then seven revert iterations coarsening by 0.1 deg per iteration","not_reported","static point-cloud map (dynamic points separated)","raw scans with SE(3) poses from LiDAR SLAM assumed to contain some error; experiments deliberately used SuMa poses (as in SemanticKITTI) rather than KITTI ground truth; a batch of sequential scans (e.g. 50 scans for a 100 m submap at 2 m spacing)","static point-cloud map and parsed dynamic points (README)","offline batch post-processing; the paper explicitly sets processing speed aside and reports no runtime or hardware (the over 10 Hz figure comes from the code README only)","https:\u002F\u002Fgithub.com\u002Fgisbi-kim\u002Fremovert","CC BY-NC-SA 4.0 (stated in README; non-commercial)",[48],{"relation":49,"title":50,"doi_or_url":51},"code_release","irapkaist\u002Fremovert (mirror: gisbi-kim\u002Fremovert)","https:\u002F\u002Fgithub.com\u002Firapkaist\u002Fremovert",{"id":5,"kind":53,"shortName":7,"title":8,"authors":54,"year":9,"venue":57,"venueType":58,"publisher":59,"volumeIssuePages":60,"doi":61,"arxivId":62,"url":63,"firstPublicDate":64,"publicationStatus":16,"metadataStatus":65,"fulltextStatus":15,"era":10,"classicReason":38,"codeUrl":45,"cluster":11,"topics":66,"mdpi":68,"verification":69,"label":6,"fulltextRoute":70,"versionRead":71,"addedByCensus":68},"method",[55,56],"Giseop Kim","Ayoung Kim","2020 IEEE\u002FRSJ International Conference on Intelligent Robots and Systems (IROS)","conference","IEEE","pp. 10758-10765","10.1109\u002Firos45743.2020.9340856",null,"https:\u002F\u002Fdoi.org\u002F10.1109\u002FIROS45743.2020.9340856","2020-10-24","metadata_verified",[11,67],"C13",false,"corrected","NTU institutional (Chrome)","Version of record, IEEE Xplore HTML full text (IROS 2020, document 9340856)",[73,80],{"category":74,"model":75,"canonical":75,"role":76,"dataset":77,"specs":78,"locator":79},"lidar","not_reported (KITTI LiDAR, 64 rays, 27 deg vertical FOV)","dataset sensor","KITTI odometry; SemanticKITTI","vertical FOV 27 deg, 64 rays","Sec. IV-A",{"category":74,"model":81,"canonical":81,"role":76,"dataset":82,"specs":83,"locator":84},"not_reported (MulRan LiDAR, 45 deg vertical FOV)","MulRan (KAIST 02)","vertical FOV 45 deg","Sec. IV-B, Fig. 7",[],{"totalRows":87,"groupCount":88,"groups":89,"others":845},90,9,[90,330,514,706],{"slug":91,"group":92,"sourceId":93,"sourceLabel":94,"table":95,"selfRows":96,"metrics":97,"seqs":107,"entrants":120,"cells":136,"outcomes":324,"locators":325,"hardware":326,"wordings":327,"notes":328},"erasor2021-table-ii","erasor2021:Table II","erasor2021","Lim et al., 2021","Table II",30,[98,102,104],{"label":99,"unit":100,"statistic":40,"alignment":101},"Preservation Rate (PR)","%","none",{"label":103,"unit":100,"statistic":40,"alignment":101},"Rejection Rate (RR)",{"label":105,"unit":106,"statistic":40,"alignment":101},"F1 score","ratio",[108,112,114,116,118],{"dataset":109,"sequence":110,"environment":111},"SemanticKITTI","00 (frames 4390-4530)","urban driving (countryside, highway, intersections)",{"dataset":109,"sequence":113,"environment":111},"01 (frames 150-250)",{"dataset":109,"sequence":115,"environment":111},"02 (frames 860-950)",{"dataset":109,"sequence":117,"environment":111},"05 (frames 2350-2670)",{"dataset":109,"sequence":119,"environment":111},"07 (frames 630-820)",[121,125,127,130,132,134],{"name":122,"methodId":123,"linkable":124,"proposed":68,"self":68},"OctoMap - 0.05","hornung2013octomap",true,{"name":126,"methodId":123,"linkable":124,"proposed":68,"self":68},"OctoMap - 0.2",{"name":128,"methodId":129,"linkable":124,"proposed":68,"self":68},"Peopleremover","schauer2018peopleremover",{"name":131,"methodId":5,"linkable":124,"proposed":68,"self":124},"Removert - RM3",{"name":133,"methodId":5,"linkable":124,"proposed":68,"self":124},"Removert - RM3+RV1",{"name":135,"methodId":93,"linkable":124,"proposed":124,"self":68},"ERASOR (Ours)",[137,141,144,147,149,151,153,155,157,159,162,164,166,169,171,173,176,178,180,182,184,186,188,190,192,194,196,198,200,202,204,206,208,210,212,214,216,218,220,222,224,226,228,230,232,234,236,238,240,242,244,246,248,250,252,254,256,258,260,262,264,266,268,270,272,274,276,278,280,282,284,286,288,290,292,294,296,298,300,302,304,306,308,310,312,314,316,318,320,322],[138,138,138,139,140,138,140,140,138],0,76.731,-1,[138,142,138,143,140,138,140,140,138],1,99.124,[138,145,138,146,140,138,140,140,138],2,0.865,[142,138,138,148,140,138,140,140,138],34.568,[142,142,138,150,140,138,140,140,138],99.979,[142,145,138,152,140,138,140,140,138],0.514,[145,138,138,154,140,138,140,140,138],37.523,[145,142,138,156,140,138,140,140,138],89.116,[145,145,138,158,140,138,140,140,138],0.528,[160,138,138,161,140,138,140,140,138],3,85.502,[160,142,138,163,140,138,140,140,138],99.354,[160,145,138,165,140,138,140,140,138],0.919,[167,138,138,168,140,138,140,140,138],4,86.829,[167,142,138,170,140,138,140,140,138],90.617,[167,145,138,172,140,138,140,140,138],0.887,[174,138,138,175,140,138,140,140,138],5,93.98,[174,142,138,177,140,138,140,140,138],97.081,[174,145,138,179,140,138,140,140,138],0.955,[138,138,142,181,140,138,140,140,138],53.163,[138,142,142,183,140,138,140,140,138],99.663,[138,145,142,185,140,138,140,140,138],0.693,[142,138,142,187,140,138,140,140,138],20.777,[142,142,142,189,140,138,140,140,138],99.863,[142,145,142,191,140,138,140,140,138],0.344,[145,138,142,193,140,138,140,140,138],36.349,[145,142,142,195,140,138,140,140,138],93.116,[145,145,142,197,140,138,140,140,138],0.523,[160,138,142,199,140,138,140,140,138],94.221,[160,142,142,201,140,138,140,140,138],93.608,[160,145,142,203,140,138,140,140,138],0.939,[167,138,142,205,140,138,140,140,138],95.815,[167,142,142,207,140,138,140,140,138],57.077,[167,145,142,209,140,138,140,140,138],0.715,[174,138,142,211,140,138,140,140,138],91.487,[174,142,142,213,140,138,140,140,138],95.383,[174,145,142,215,140,138,140,140,138],0.934,[138,138,145,217,140,138,140,140,138],54.112,[138,142,145,219,140,138,140,140,138],98.769,[138,145,145,221,140,138,140,140,138],0.699,[142,138,145,223,140,138,140,140,138],23.746,[142,142,145,225,140,138,140,140,138],99.792,[142,145,145,227,140,138,140,140,138],0.384,[145,138,145,229,140,138,140,140,138],29.037,[145,142,145,231,140,138,140,140,138],94.527,[145,145,145,233,140,138,140,140,138],0.444,[160,138,145,235,140,138,140,140,138],76.319,[160,142,145,237,140,138,140,140,138],96.799,[160,145,145,239,140,138,140,140,138],0.853,[167,138,145,241,140,138,140,140,138],83.293,[167,142,145,243,140,138,140,140,138],88.371,[167,145,145,245,140,138,140,140,138],0.858,[174,138,145,247,140,138,140,140,138],87.731,[174,142,145,249,140,138,140,140,138],97.008,[174,145,145,251,140,138,140,140,138],0.921,[138,138,160,253,140,138,140,140,138],76.341,[138,142,160,255,140,138,140,140,138],96.785,[138,145,160,257,140,138,140,140,138],0.854,[142,138,160,259,140,138,140,140,138],33.904,[142,142,160,261,140,138,140,140,138],99.882,[142,145,160,263,140,138,140,140,138],0.506,[145,138,160,265,140,138,140,140,138],38.495,[145,142,160,267,140,138,140,140,138],90.631,[145,145,160,269,140,138,140,140,138],0.54,[160,138,160,271,140,138,140,140,138],86.9,[160,142,160,273,140,138,140,140,138],87.88,[160,145,160,275,140,138,140,140,138],0.874,[167,138,160,277,140,138,140,140,138],88.17,[167,142,160,279,140,138,140,140,138],79.981,[167,145,160,281,140,138,140,140,138],0.839,[174,138,160,283,140,138,140,140,138],88.73,[174,142,160,285,140,138,140,140,138],98.262,[174,145,160,287,140,138,140,140,138],0.933,[138,138,167,289,140,138,140,140,138],77.838,[138,142,167,291,140,138,140,140,138],96.938,[138,145,167,293,140,138,140,140,138],0.863,[142,138,167,295,140,138,140,140,138],38.183,[142,142,167,297,140,138,140,140,138],99.565,[142,145,167,299,140,138,140,140,138],0.552,[145,138,167,301,140,138,140,140,138],34.772,[145,142,167,303,140,138,140,140,138],91.983,[145,145,167,305,140,138,140,140,138],0.505,[160,138,167,307,140,138,140,140,138],80.689,[160,142,167,309,140,138,140,140,138],98.822,[160,145,167,311,140,138,140,140,138],0.888,[167,138,167,313,140,138,140,140,138],82.038,[167,142,167,315,140,138,140,140,138],95.504,[167,145,167,317,140,138,140,140,138],0.883,[174,138,167,319,140,138,140,140,138],90.624,[174,142,167,321,140,138,140,140,138],99.271,[174,145,167,323,140,138,140,140,138],0.948,[],[95],[],[],[329],"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":331,"group":332,"sourceId":333,"sourceLabel":334,"table":95,"selfRows":335,"metrics":336,"seqs":346,"entrants":360,"cells":368,"outcomes":508,"locators":509,"hardware":510,"wordings":511,"notes":512},"yang2024lifelong-table-ii","yang2024lifelong:Table II","yang2024lifelong","Yang et al., 2024",18,[337,339,341,342,344,345],{"label":338,"unit":106,"statistic":40,"alignment":101},"PR (preservation rate)",{"label":340,"unit":106,"statistic":40,"alignment":101},"RR (rejection rate)",{"label":105,"unit":106,"statistic":40,"alignment":101},{"label":338,"unit":106,"statistic":343,"alignment":101},"mean",{"label":340,"unit":106,"statistic":343,"alignment":101},{"label":105,"unit":106,"statistic":343,"alignment":101},[347,350,352,354,356,358],{"dataset":109,"sequence":348,"environment":349},"00","urban driving, vehicle-mounted LiDAR",{"dataset":109,"sequence":351,"environment":349},"01",{"dataset":109,"sequence":353,"environment":349},"02",{"dataset":109,"sequence":355,"environment":349},"05",{"dataset":109,"sequence":357,"environment":349},"07",{"dataset":109,"sequence":359,"environment":349},"mean of sequences 00, 01, 02, 05, 07",[361,363,364,366],{"name":362,"methodId":93,"linkable":124,"proposed":68,"self":68},"ERASOR",{"name":7,"methodId":5,"linkable":124,"proposed":68,"self":124},{"name":365,"methodId":62,"linkable":68,"proposed":68,"self":68},"Ground-Octomap",{"name":367,"methodId":333,"linkable":124,"proposed":124,"self":68},"Ours",[369,371,373,375,377,379,381,383,385,387,389,391,393,395,397,399,401,403,405,407,409,411,413,415,417,419,421,423,425,427,429,431,433,435,437,439,441,443,445,447,449,451,453,455,456,458,459,460,462,464,466,468,470,472,474,476,478,480,482,484,486,488,490,492,494,496,497,499,501,503,505,506],[138,138,138,370,140,138,140,140,138],0.9172,[138,142,138,372,140,138,140,140,138],0.97,[138,145,138,374,140,138,140,140,138],0.9429,[142,138,138,376,140,138,140,140,138],0.9328,[142,142,138,378,140,138,140,140,138],0.7663,[142,145,138,380,140,138,140,140,138],0.8414,[145,138,138,382,140,138,140,140,138],0.7765,[145,142,138,384,140,138,140,140,138],0.9526,[145,145,138,386,140,138,140,140,138],0.8556,[160,138,138,388,140,138,140,140,138],0.9471,[160,142,138,390,140,138,140,140,138],0.9712,[160,145,138,392,140,138,140,140,138],0.959,[138,138,142,394,140,138,140,140,138],0.9193,[138,142,142,396,140,138,140,140,138],0.9463,[138,145,142,398,140,138,140,140,138],0.9326,[142,138,142,400,140,138,140,140,138],0.9579,[142,142,142,402,140,138,140,140,138],0.6688,[142,145,142,404,140,138,140,140,138],0.7877,[145,138,142,406,140,138,140,140,138],0.8475,[145,142,142,408,140,138,140,140,138],0.7337,[145,145,142,410,140,138,140,140,138],0.7865,[160,138,142,412,140,138,140,140,138],0.9425,[160,142,142,414,140,138,140,140,138],0.9528,[160,145,142,416,140,138,140,140,138],0.9477,[138,138,145,418,140,138,140,140,138],0.8108,[138,142,145,420,140,138,140,140,138],0.9911,[138,145,145,422,140,138,140,140,138],0.8919,[142,138,145,424,140,138,140,140,138],0.8531,[142,142,145,426,140,138,140,140,138],0.8222,[142,145,145,428,140,138,140,140,138],0.8374,[145,138,145,430,140,138,140,140,138],0.9479,[145,142,145,432,140,138,140,140,138],0.6277,[145,145,145,434,140,138,140,140,138],0.7553,[160,138,145,436,140,138,140,140,138],0.9421,[160,142,145,438,140,138,140,140,138],0.9035,[160,145,145,440,140,138,140,140,138],0.9224,[138,138,160,442,140,138,140,140,138],0.8698,[138,142,160,444,140,138,140,140,138],0.9788,[138,145,160,446,140,138,140,140,138],0.9211,[142,138,160,448,140,138,140,140,138],0.9223,[142,142,160,450,140,138,140,140,138],0.6757,[142,145,160,452,140,138,140,140,138],0.78,[145,138,160,454,140,138,140,140,138],0.6956,[145,142,160,203,140,138,140,140,138],[145,145,160,457,140,138,140,140,138],0.7992,[160,138,160,412,140,138,140,140,138],[160,142,160,390,140,138,140,140,138],[160,145,160,461,140,138,140,140,138],0.9566,[138,138,167,463,140,138,140,140,138],0.92,[138,142,167,465,140,138,140,140,138],0.9833,[138,145,167,467,140,138,140,140,138],0.9506,[142,138,167,469,140,138,140,140,138],0.8482,[142,142,167,471,140,138,140,140,138],0.5758,[142,145,167,473,140,138,140,140,138],0.686,[145,138,167,475,140,138,140,140,138],0.5396,[145,142,167,477,140,138,140,140,138],0.9081,[145,145,167,479,140,138,140,140,138],0.6769,[160,138,167,481,140,138,140,140,138],0.9768,[160,142,167,483,140,138,140,140,138],0.941,[160,145,167,485,140,138,140,140,138],0.9586,[138,160,174,487,140,138,140,140,138],0.8874,[138,167,174,489,140,138,140,140,138],0.9739,[138,174,174,491,140,138,140,140,138],0.9278,[142,160,174,493,140,138,140,140,138],0.9029,[142,167,174,495,140,138,140,140,138],0.7017,[142,174,174,410,140,138,140,140,138],[145,160,174,498,140,138,140,140,138],0.7614,[145,167,174,500,140,138,140,140,138],0.8322,[145,174,174,502,140,138,140,140,138],0.7747,[160,160,174,504,140,138,140,140,138],0.9502,[160,167,174,430,140,138,140,140,138],[160,174,174,507,140,138,140,140,138],0.9488,[],[95],[],[],[513],"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":515,"group":516,"sourceId":517,"sourceLabel":518,"table":519,"selfRows":520,"metrics":521,"seqs":528,"entrants":544,"cells":558,"outcomes":700,"locators":701,"hardware":702,"wordings":703,"notes":704},"dufomap2024-table-i","dufomap2024:Table I","dufomap2024","Duberg et al., 2024","Table I",12,[522,524,526],{"label":523,"unit":100,"statistic":40,"alignment":101},"SA (static accuracy, share of static points correctly kept)",{"label":525,"unit":100,"statistic":40,"alignment":101},"DA (dynamic accuracy, share of dynamic points correctly labelled)",{"label":527,"unit":100,"statistic":40,"alignment":101},"AA (associated accuracy, sqrt(SA x DA))",[529,533,536,540],{"dataset":530,"sequence":531,"environment":532},"KITTI (SemanticKITTI labels and poses)","00 small town","small town (HDL-64E)",{"dataset":530,"sequence":534,"environment":535},"01 highway","highway (HDL-64E)",{"dataset":537,"sequence":538,"environment":539},"Argoverse 2","big city","urban big city (two VLP-32C)",{"dataset":541,"sequence":542,"environment":543},"Semi-indoor (self-collected)","semi-indoor","highly structured semi-indoor area, sparse 16-channel LiDAR (VLP-16)",[545,547,549,551,553,556],{"name":546,"methodId":5,"linkable":124,"proposed":68,"self":124},"Removert [8]",{"name":548,"methodId":93,"linkable":124,"proposed":68,"self":68},"ERASOR [9]",{"name":550,"methodId":123,"linkable":124,"proposed":68,"self":68},"OctoMap [16]",{"name":552,"methodId":517,"linkable":124,"proposed":124,"self":68},"DUFOMap (Ours)",{"name":554,"methodId":555,"linkable":124,"proposed":68,"self":68},"Dynablox [17]","dynablox2023",{"name":557,"methodId":517,"linkable":124,"proposed":124,"self":68},"DUFOMap* (Ours, 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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":707,"group":708,"sourceId":709,"sourceLabel":710,"table":519,"selfRows":520,"metrics":711,"seqs":718,"entrants":731,"cells":742,"outcomes":839,"locators":840,"hardware":841,"wordings":842,"notes":843},"dynbench2023-table-i","dynbench2023:Table I","dynbench2023","Zhang et al., 2023a",[712,714,716],{"label":713,"unit":100,"statistic":40,"alignment":101},"SA (static accuracy)",{"label":715,"unit":100,"statistic":40,"alignment":101},"DA (dynamic accuracy)",{"label":717,"unit":100,"statistic":40,"alignment":101},"AA (associated accuracy)",[719,723,725,728],{"dataset":720,"sequence":721,"environment":722},"KITTI (SemanticKITTI labels)","sequence 00","small town (SemanticKITTI)",{"dataset":720,"sequence":724,"environment":722},"sequence 05",{"dataset":726,"sequence":538,"environment":727},"Argoverse 2.0","car, urban (two VLP-32C)",{"dataset":729,"sequence":542,"environment":730},"Semi-indoor (authors' custom)","semi-indoor, one sparse VLP-16",[732,734,736,738,740],{"name":733,"methodId":5,"linkable":124,"proposed":68,"self":124},"Removert* [5]",{"name":735,"methodId":93,"linkable":124,"proposed":68,"self":68},"ERASOR* [16]",{"name":737,"methodId":123,"linkable":124,"proposed":68,"self":68},"Octomap [8]",{"name":739,"methodId":709,"linkable":68,"proposed":124,"self":68},"Octomap w G",{"name":741,"methodId":709,"linkable":68,"proposed":124,"self":68},"Octomap w GF",[743,744,745,746,748,750,752,753,754,755,756,757,758,759,760,761,763,765,767,768,769,770,771,772,773,774,775,776,778,780,782,784,786,788,789,790,791,793,795,797,799,801,803,805,807,809,811,813,815,817,819,821,823,825,827,829,831,833,835,837],[138,138,138,560,140,138,140,140,138],[138,142,138,562,140,138,140,140,138],[138,145,138,564,140,138,140,140,138],[138,138,142,747,140,138,140,140,138],99.42,[138,142,142,749,140,138,140,140,138],22.28,[138,145,142,751,140,138,140,140,138],47.06,[138,138,145,572,140,138,140,140,138],[138,142,145,574,140,138,140,140,138],[138,145,145,576,140,138,140,140,138],[138,138,160,578,140,138,140,140,138],[138,142,160,580,140,138,140,140,138],[138,145,160,582,140,138,140,140,138],[142,138,138,584,140,138,140,140,138],[142,142,138,586,140,138,140,140,138],[142,145,138,588,140,138,140,140,138],[142,138,142,762,140,138,140,140,138],69.4,[142,142,142,764,140,138,140,140,138],99.06,[142,145,142,766,140,138,140,140,138],82.92,[142,138,145,596,140,138,140,140,138],[142,142,145,598,140,138,140,140,138],[142,145,145,600,140,138,140,140,138],[142,138,160,602,140,138,140,140,138],[142,142,160,604,140,138,140,140,138],[142,145,160,606,140,138,140,140,138],[145,138,138,608,140,138,140,140,138],[145,142,138,610,140,138,140,140,138],[145,145,138,612,140,138,140,140,138],[145,138,142,777,140,138,140,140,138],66.28,[145,142,142,779,140,138,140,140,138],99.24,[145,145,142,781,140,138,140,140,138],81.1,[145,138,145,783,140,138,140,140,138],65.91,[145,142,145,785,140,138,140,140,138],96.7,[145,145,145,787,140,138,140,140,138],79.84,[145,138,160,626,140,138,140,140,138],[145,142,160,628,140,138,140,140,138],[145,145,160,630,140,138,140,140,138],[160,138,138,792,140,138,140,140,138],85.92,[160,142,138,794,140,138,140,140,138],98.88,[160,145,138,796,140,138,140,140,138],92.17,[160,138,142,798,140,138,140,140,138],86.15,[160,142,142,800,140,138,140,140,138],98.46,[160,145,142,802,140,138,140,140,138],92.1,[160,138,145,804,140,138,140,140,138],76.38,[160,142,145,806,140,138,140,140,138],86.26,[160,145,145,808,140,138,140,140,138],81.17,[160,138,160,810,140,138,140,140,138],94.95,[160,142,160,812,140,138,140,140,138],73.95,[160,145,160,814,140,138,140,140,138],83.8,[167,138,138,816,140,138,140,140,138],93.06,[167,142,138,818,140,138,140,140,138],98.67,[167,145,138,820,140,138,140,140,138],95.83,[167,138,142,822,140,138,140,140,138],93.54,[167,142,142,824,140,138,140,140,138],92.48,[167,145,142,826,140,138,140,140,138],93.01,[167,138,145,828,140,138,140,140,138],82.66,[167,142,145,830,140,138,140,140,138],82.44,[167,145,145,832,140,138,140,140,138],82.55,[167,138,160,834,140,138,140,140,138],96.79,[167,142,160,836,140,138,140,140,138],73.5,[167,145,160,838,140,138,140,140,138],84.34,[],[519],[],[],[844],"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",[846,851,858,863,867],{"group":847,"slug":848,"sourceLabel":518,"table":849,"selfRows":88,"datasets":850},"dufomap2024:Table III","dufomap2024-table-iii","Table III",[720],{"group":852,"slug":853,"sourceLabel":854,"table":855,"selfRows":167,"datasets":856},"lamm2025:Table IV","lamm2025-table-iv","Wei et al., 2025b","Table IV",[857],"HeLiPR",{"group":859,"slug":860,"sourceLabel":518,"table":95,"selfRows":145,"datasets":861},"dufomap2024:Table II","dufomap2024-table-ii",[862,541],"KITTI",{"group":864,"slug":865,"sourceLabel":710,"table":95,"selfRows":145,"datasets":866},"dynbench2023:Table II","dynbench2023-table-ii",[40],{"group":868,"slug":869,"sourceLabel":94,"table":849,"selfRows":142,"datasets":870},"erasor2021:Table III","erasor2021-table-iii",[109],1790510663381]