[{"data":1,"prerenderedAt":1045},["ShallowReactive",2],{"method-hornung2013octomap":3},{"method":4,"reference":51,"equipment":74,"figures":107,"results":108},{"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":24,"limitations":28,"sensors":31,"platform":36,"estimator":39,"association":40,"timeModel":41,"deskew":42,"loopClosure":41,"globalOptimization":43,"mapRepresentation":44,"prior":45,"outputGeometry":46,"compute":47,"codeUrl":48,"codeLicense":49,"relatedVersions":50},"hornung2013octomap","Hornung et al., 2013","OctoMap","OctoMap: an efficient probabilistic 3D mapping framework based on octrees",2013,"classic","C12","map_representation_or_reconstruction","OctoMap 以八元樹（octree）儲存體素的機率佔據值（log-odds），同時表示已佔據、空與未知空間；感測器原點到端點之間的射線更新為空，端點更新為佔據。作者加入機率上下限夾制（clamping）與樹狀剪枝壓縮，使地圖精簡並可做多解析度查詢。","Probabilistic occupancy mapping in an octree that represents occupied, free and unknown space with clamped log-odds updates and lossless compression.","full_text_reviewed","peer_reviewed_published","background","未在營建場域驗證；測試場景為弗萊堡大學 079 館走廊（43.7 m x 18.2 m x 3.3 m）、校園戶外（292 m x 167 m x 28 m）與 New College。其「未知空間」表示可用於掃描覆蓋與缺漏判斷，但 Table 1 的準確率只衡量體素佔據狀態與掃描本身的一致性，並非對獨立量測基準的幾何誤差，佔據體素也不是量測級表面（推論）。",[20,21,22,23],"public_benchmark","simulation","controlled_experiment","completed_building",[25,26,27],"[\"Explicitly represents free and unknown space, which point clouds and 2.5D maps do not (Sec. 1-2).\", \"Compact maps via pruning","with 16 depth levels at 1 cm resolution a map spans 655.36 m per dimension (Sec. 4).\", \"97.27% to 98.79% of cells agree with the scans, and 95.80% to 98.46% under 80\u002F20 cross-validation (Sec. 5.3, Table 1).\", \"Freiburg campus at 10 cm: 5162.90 MB for a full 3D grid versus 990.66 MB for the pruned octree and a 13.82 MB lossy map file (Table 2).\", \"Scans of about 90,000 to 250,000 points integrate in under a second","the full-resolution Freiburg campus map (1,087,014 occupied and 3,377,882 free leaves) is traversed in 51 ms (Sec. 5.5).\", \"Pruning with clamping improves compression by up to 44% (Sec. 3.4).\"]",[29,30],"[\"Sensor noise and discretization errors produce differing probabilities that interfere with compression relying on identical node values (Sec. 3.4).\", \"Ray-casting discretization during shallow-angle sensor sweeps can mark occupied cells as free and create holes","the authors mitigate this by treating a sweep as one point cloud and never freeing an endpoint voxel in the same update (Sec. 5.1, Figs. 10-11).\", \"Clamping makes compression lossy near probabilities 0 and 1 (Sec. 3.2, Sec. 3.4).\", \"Remaining map error is attributed to sensor noise, discretization effects or imperfect scan alignment (Sec. 5.3).\", \"In confined spaces an uncompressed octree can need more memory than an optimally aligned 3D grid, e.g. FR-079 at 10 cm: 10.87 MB versus 10.01 MB (Sec. 5.4, Table 2).\", \"(inference) The accuracy metric measures agreement with the same or held-out scans, not with an independent survey reference, so it does not bound geometric error.\"]",[32,33,34,35],"[\"2D laser range finder on a pan-tilt unit (SICK LMS, FR-079 corridor)\", \"two fixed laser scanners sweeping to the left and right of the robot (New College Epoch C","models not stated)\", \"dense 3D laser scans (Freiburg campus, 81 scans","sensor model not stated","ranges up to 50 m)\", \"RGB-D camera (Microsoft Kinect, freiburg1_360 sequence)\"]",[37,38],"[\"wheeled robot (Pioneer2 AT, FR-079 corridor)\", \"hand-held Microsoft Kinect (freiburg1_360, TUM RGB-D)\", \"robot carrying two fixed sweeping laser scanners (New College Epoch C","platform type not stated in the paper)\", \"Freiburg campus dataset (platform not stated in the paper)\"]","not_applicable (mapping with externally supplied poses: FR-079 odometry refined by 3D scan matching, New College trajectory from visual odometry (Sibley et al. 2009), freiburg1_360 aligned by RGB-D SLAM)","ray casting from sensor origin to endpoints with a 3D Bresenham-type voxel traversal; endpoints updated as occupied (l_occ = 0.85, p = 0.7) and traversed voxels as free (l_free = -0.4, p = 0.4); an endpoint voxel is never freed within the same sweep update","not_applicable","not_reported","none","octree of voxels with log-odds occupancy, clamping thresholds, lossless pruning and multi-resolution queries","sensor poses from an external source","occupied\u002Ffree\u002Funknown voxel map at chosen resolution (map files); not a surface model","single core of an Intel Core i7-2600 (3.4 GHz) desktop CPU for all runtime tests; memory accounted for a 32-bit architecture (inner node 40 B, leaf 8 B)","https:\u002F\u002Fgithub.com\u002FOctoMap\u002Foctomap","New BSD (octomap library); GPL (octovis viewer), per repository README",[],{"id":5,"kind":52,"shortName":7,"title":8,"authors":53,"year":9,"venue":59,"venueType":60,"publisher":61,"volumeIssuePages":62,"doi":63,"arxivId":64,"url":65,"firstPublicDate":66,"publicationStatus":16,"metadataStatus":67,"fulltextStatus":15,"era":10,"classicReason":68,"codeUrl":48,"cluster":11,"topics":69,"mdpi":70,"verification":71,"label":6,"fulltextRoute":72,"versionRead":73,"addedByCensus":70},"method",[54,55,56,57,58],"Armin Hornung","Kai M. Wurm","Maren Bennewitz","Cyrill Stachniss","Wolfram Burgard","Autonomous Robots","journal","Springer","34(3):189-206","10.1007\u002Fs10514-012-9321-0",null,"https:\u002F\u002Fwww.arminhornung.de\u002FResearch\u002Fpub\u002Fhornung13auro.pdf","2013-02-07","metadata_verified","reproducible baseline: probabilistic occupancy octree that explicitly models free and unknown space; used as the baseline in Voxblox and VDBFusion evaluations.",[11],false,"corrected","author copy","Author preprint (accepted manuscript, 17 pp., header 'Preprint, final version available at DOI 10.1007\u002Fs10514-012-9321-0'), hosted at arminhornung.de; Springer version of record not compared",[75,82,86,90,95,101],{"category":76,"model":77,"canonical":77,"role":78,"dataset":79,"specs":80,"locator":81},"lidar","SICK LMS","dataset sensor","FR-079 corridor","2D laser range finder on a pan-tilt unit; beam range limited to 10 m for this dataset; 66 3D scans, 6 million end points","Sec. 5.2",{"category":83,"model":84,"canonical":84,"role":78,"dataset":79,"specs":85,"locator":81},"platform","Pioneer2 AT","mobile robot carrying the pan-tilt laser",{"category":76,"model":87,"canonical":87,"role":78,"dataset":88,"specs":89,"locator":81},"two fixed sweeping laser scanners (model not reported)","New College (Epoch C)","sweeping to the left and right side of the robot; 14 million end points",{"category":76,"model":91,"canonical":91,"role":78,"dataset":92,"specs":93,"locator":94},"laser scanner (model and scanning mechanism not reported)","Freiburg campus","81 dense 3D scans, 20 million end points, full laser range up to 50 m","Sec. 5.2, Sec. 5.5.1",{"category":96,"model":97,"canonical":97,"role":78,"dataset":98,"specs":99,"locator":100},"rgbd","Microsoft Kinect","TUM RGB-D freiburg1_360","hand-held; colored point clouds, 210 million end points","Sec. 3.5.1, Sec. 5.2",{"category":102,"model":103,"canonical":103,"role":104,"dataset":64,"specs":105,"locator":106},"compute","Intel Core i7-2600","compute for runtime","3.4 GHz; single core used","Sec. 5.5",[],{"totalRows":109,"groupCount":110,"groups":111,"others":969},170,18,[112,289,520,778],{"slug":113,"group":114,"sourceId":5,"sourceLabel":6,"table":115,"selfRows":116,"metrics":117,"seqs":135,"entrants":160,"cells":172,"outcomes":282,"locators":283,"hardware":284,"wordings":286,"notes":287},"hornung2013octomap-table-2","hornung2013octomap:Table 2","Table 2",50,[118,121,123,125,127,129,131,133],{"label":119,"unit":120,"statistic":42,"alignment":42},"Memory w. octree compression [MB], None","MB",{"label":122,"unit":120,"statistic":42,"alignment":42},"Memory w. octree compression [MB], Pruned",{"label":124,"unit":120,"statistic":42,"alignment":42},"Memory w. octree compression [MB], Max. likelih.",{"label":126,"unit":120,"statistic":42,"alignment":42},"File size [MB], Full",{"label":128,"unit":120,"statistic":42,"alignment":42},"File size [MB], Lossy",{"label":130,"unit":120,"statistic":42,"alignment":42},"Memory w. octree compression [MB], None (*) with color",{"label":132,"unit":120,"statistic":42,"alignment":42},"Memory w. octree compression [MB], Pruned (*) with color",{"label":134,"unit":120,"statistic":42,"alignment":42},"File size [MB], Full (*) with color",[136,139,141,144,146,148,151,153,155,158],{"dataset":79,"sequence":137,"environment":138},"43.7 x 18.2 x 3.3 m, resolution 5 cm","indoor university corridor",{"dataset":79,"sequence":140,"environment":138},"43.7 x 18.2 x 3.3 m, resolution 10 cm",{"dataset":92,"sequence":142,"environment":143},"292 x 167 x 28 m, resolution 10 cm","outdoor campus",{"dataset":92,"sequence":145,"environment":143},"292 x 167 x 28 m, resolution 20 cm",{"dataset":92,"sequence":147,"environment":143},"292 x 167 x 28 m, resolution 80 cm",{"dataset":88,"sequence":149,"environment":150},"250 x 161 x 33 m, resolution 10 cm","large-scale outdoor",{"dataset":88,"sequence":152,"environment":150},"250 x 161 x 33 m, resolution 20 cm",{"dataset":88,"sequence":154,"environment":150},"250 x 161 x 33 m, resolution 80 cm",{"dataset":98,"sequence":156,"environment":157},"7.9 x 7.3 x 4.6 m, resolution 2 cm","indoor office",{"dataset":98,"sequence":159,"environment":157},"7.9 x 7.3 x 4.6 m, resolution 5 cm",[161,164,166,168,170],{"name":162,"methodId":5,"linkable":163,"proposed":163,"self":163},"OctoMap, no compression",true,{"name":165,"methodId":5,"linkable":163,"proposed":163,"self":163},"OctoMap, pruned",{"name":167,"methodId":5,"linkable":163,"proposed":163,"self":163},"OctoMap, maximum-likelihood compression",{"name":169,"methodId":5,"linkable":163,"proposed":163,"self":163},"OctoMap file, full probabilistic",{"name":171,"methodId":5,"linkable":163,"proposed":163,"self":163},"OctoMap file, lossy maximum-likelihood",[173,177,180,183,186,189,191,193,195,197,199,201,203,205,207,209,211,213,215,217,218,220,222,224,226,228,231,233,235,237,239,242,244,246,248,250,253,255,257,259,261,264,266,268,270,272,275,276,278,280],[174,174,174,175,176,174,174,176,174],0,73.55,-1,[178,178,174,179,176,174,174,176,174],1,41.62,[181,181,174,182,176,174,174,176,174],2,24.72,[184,184,174,185,176,174,176,176,174],3,15.76,[187,187,174,188,176,174,176,176,174],4,0.67,[174,174,178,190,176,174,174,176,174],10.87,[178,178,178,192,176,174,174,176,174],7.22,[181,181,178,194,176,174,174,176,174],5.02,[184,184,178,196,176,174,176,176,174],2.7,[187,187,178,198,176,174,176,176,174],0.14,[174,174,181,200,176,174,174,176,174],1257.57,[178,178,181,202,176,174,174,176,174],990.66,[181,181,181,204,176,174,174,176,174],504.76,[184,184,181,206,176,174,176,176,174],379.7,[187,187,181,208,176,174,176,176,174],13.82,[174,174,184,210,176,174,174,176,174],187.93,[178,178,184,212,176,174,174,176,174],130.24,[181,181,184,214,176,174,174,176,174],74.12,[184,184,184,216,176,174,176,176,174],49.68,[187,187,184,181,176,174,176,176,174],[174,174,187,219,176,174,174,176,174],4.55,[178,178,187,221,176,174,174,176,174],4.12,[181,181,187,223,176,174,174,176,174],3.09,[184,184,187,225,176,174,176,176,174],1.53,[187,187,187,227,176,174,176,176,174],0.08,[174,174,229,230,176,174,174,176,174],5,607.92,[178,178,229,232,176,174,174,176,174],395.42,[181,181,229,234,176,174,174,176,174],230.33,[184,184,229,236,176,174,176,176,174],148.75,[187,187,229,238,176,174,176,176,174],6.4,[174,174,240,241,176,174,174,176,174],6,91.33,[178,178,240,243,176,174,174,176,174],50.57,[181,181,240,245,176,174,174,176,174],35.95,[184,184,240,247,176,174,176,176,174],18.65,[187,187,240,249,176,174,176,176,174],0.99,[174,174,251,252,176,174,174,176,174],7,2.34,[178,178,251,254,176,174,174,176,174],1.79,[181,181,251,256,176,174,174,176,174],1.69,[184,184,251,258,176,174,176,176,174],0.63,[187,187,251,260,176,174,176,176,174],0.05,[174,229,262,263,176,174,174,176,174],8,159.97,[178,240,262,265,176,174,174,176,174],45.52,[181,181,262,267,176,174,174,176,174],20.05,[184,251,262,269,176,174,176,176,174],21.59,[187,187,262,271,176,174,176,176,174],0.52,[174,229,273,274,176,174,174,176,174],9,11.24,[178,240,273,219,176,174,174,176,174],[181,181,273,277,176,174,174,176,174],2.52,[184,251,273,279,176,174,176,176,174],2.11,[187,187,273,281,176,174,176,176,174],0.07,[],[115],[285],"32-bit memory accounting",[],[288],"Memory on a 32-bit architecture: full 3D grid (minimal bounding box, one float per cell) versus OctoMap without compression, pruned, and maximum-likelihood compressed; file sizes for the full probabilistic and the lossy maximum-likelihood binary format; (*) freiburg1_360 voxels include full color",{"slug":290,"group":291,"sourceId":292,"sourceLabel":293,"table":294,"selfRows":295,"metrics":296,"seqs":305,"entrants":318,"cells":333,"outcomes":514,"locators":515,"hardware":516,"wordings":517,"notes":518},"erasor2021-table-ii","erasor2021:Table II","erasor2021","Lim et al., 2021","Table II",30,[297,300,302],{"label":298,"unit":299,"statistic":42,"alignment":43},"Preservation Rate (PR)","%",{"label":301,"unit":299,"statistic":42,"alignment":43},"Rejection Rate (RR)",{"label":303,"unit":304,"statistic":42,"alignment":43},"F1 score","ratio",[306,310,312,314,316],{"dataset":307,"sequence":308,"environment":309},"SemanticKITTI","00 (frames 4390-4530)","urban driving (countryside, highway, intersections)",{"dataset":307,"sequence":311,"environment":309},"01 (frames 150-250)",{"dataset":307,"sequence":313,"environment":309},"02 (frames 860-950)",{"dataset":307,"sequence":315,"environment":309},"05 (frames 2350-2670)",{"dataset":307,"sequence":317,"environment":309},"07 (frames 630-820)",[319,321,323,326,329,331],{"name":320,"methodId":5,"linkable":163,"proposed":70,"self":163},"OctoMap - 0.05",{"name":322,"methodId":5,"linkable":163,"proposed":70,"self":163},"OctoMap - 0.2",{"name":324,"methodId":325,"linkable":163,"proposed":70,"self":70},"Peopleremover","schauer2018peopleremover",{"name":327,"methodId":328,"linkable":163,"proposed":70,"self":70},"Removert - RM3","removert2020",{"name":330,"methodId":328,"linkable":163,"proposed":70,"self":70},"Removert - RM3+RV1",{"name":332,"methodId":292,"linkable":163,"proposed":163,"self":70},"ERASOR (Ours)",[334,336,338,340,342,344,346,348,350,352,354,356,358,360,362,364,366,368,370,372,374,376,378,380,382,384,386,388,390,392,394,396,398,400,402,404,406,408,410,412,414,416,418,420,422,424,426,428,430,432,434,436,438,440,442,444,446,448,450,452,454,456,458,460,462,464,466,468,470,472,474,476,478,480,482,484,486,488,490,492,494,496,498,500,502,504,506,508,510,512],[174,174,174,335,176,174,176,176,174],76.731,[174,178,174,337,176,174,176,176,174],99.124,[174,181,174,339,176,174,176,176,174],0.865,[178,174,174,341,176,174,176,176,174],34.568,[178,178,174,343,176,174,176,176,174],99.979,[178,181,174,345,176,174,176,176,174],0.514,[181,174,174,347,176,174,176,176,174],37.523,[181,178,174,349,176,174,176,176,174],89.116,[181,181,174,351,176,174,176,176,174],0.528,[184,174,174,353,176,174,176,176,174],85.502,[184,178,174,355,176,174,176,176,174],99.354,[184,181,174,357,176,174,176,176,174],0.919,[187,174,174,359,176,174,176,176,174],86.829,[187,178,174,361,176,174,176,176,174],90.617,[187,181,174,363,176,174,176,176,174],0.887,[229,174,174,365,176,174,176,176,174],93.98,[229,178,174,367,176,174,176,176,174],97.081,[229,181,174,369,176,174,176,176,174],0.955,[174,174,178,371,176,174,176,176,174],53.163,[174,178,178,373,176,174,176,176,174],99.663,[174,181,178,375,176,174,176,176,174],0.693,[178,174,178,377,176,174,176,176,174],20.777,[178,178,178,379,176,174,176,176,174],99.863,[178,181,178,381,176,174,176,176,174],0.344,[181,174,178,383,176,174,176,176,174],36.349,[181,178,178,385,176,174,176,176,174],93.116,[181,181,178,387,176,174,176,176,174],0.523,[184,174,178,389,176,174,176,176,174],94.221,[184,178,178,391,176,174,176,176,174],93.608,[184,181,178,393,176,174,176,176,174],0.939,[187,174,178,395,176,174,176,176,174],95.815,[187,178,178,397,176,174,176,176,174],57.077,[187,181,178,399,176,174,176,176,174],0.715,[229,174,178,401,176,174,176,176,174],91.487,[229,178,178,403,176,174,176,176,174],95.383,[229,181,178,405,176,174,176,176,174],0.934,[174,174,181,407,176,174,176,176,174],54.112,[174,178,181,409,176,174,176,176,174],98.769,[174,181,181,411,176,174,176,176,174],0.699,[178,174,181,413,176,174,176,176,174],23.746,[178,178,181,415,176,174,176,176,174],99.792,[178,181,181,417,176,174,176,176,174],0.384,[181,174,181,419,176,174,176,176,174],29.037,[181,178,181,421,176,174,176,176,174],94.527,[181,181,181,423,176,174,176,176,174],0.444,[184,174,181,425,176,174,176,176,174],76.319,[184,178,181,427,176,174,176,176,174],96.799,[184,181,181,429,176,174,176,176,174],0.853,[187,174,181,431,176,174,176,176,174],83.293,[187,178,181,433,176,174,176,176,174],88.371,[187,181,181,435,176,174,176,176,174],0.858,[229,174,181,437,176,174,176,176,174],87.731,[229,178,181,439,176,174,176,176,174],97.008,[229,181,181,441,176,174,176,176,174],0.921,[174,174,184,443,176,174,176,176,174],76.341,[174,178,184,445,176,174,176,176,174],96.785,[174,181,184,447,176,174,176,176,174],0.854,[178,174,184,449,176,174,176,176,174],33.904,[178,178,184,451,176,174,176,176,174],99.882,[178,181,184,453,176,174,176,176,174],0.506,[181,174,184,455,176,174,176,176,174],38.495,[181,178,184,457,176,174,176,176,174],90.631,[181,181,184,459,176,174,176,176,174],0.54,[184,174,184,461,176,174,176,176,174],86.9,[184,178,184,463,176,174,176,176,174],87.88,[184,181,184,465,176,174,176,176,174],0.874,[187,174,184,467,176,174,176,176,174],88.17,[187,178,184,469,176,174,176,176,174],79.981,[187,181,184,471,176,174,176,176,174],0.839,[229,174,184,473,176,174,176,176,174],88.73,[229,178,184,475,176,174,176,176,174],98.262,[229,181,184,477,176,174,176,176,174],0.933,[174,174,187,479,176,174,176,176,174],77.838,[174,178,187,481,176,174,176,176,174],96.938,[174,181,187,483,176,174,176,176,174],0.863,[178,174,187,485,176,174,176,176,174],38.183,[178,178,187,487,176,174,176,176,174],99.565,[178,181,187,489,176,174,176,176,174],0.552,[181,174,187,491,176,174,176,176,174],34.772,[181,178,187,493,176,174,176,176,174],91.983,[181,181,187,495,176,174,176,176,174],0.505,[184,174,187,497,176,174,176,176,174],80.689,[184,178,187,499,176,174,176,176,174],98.822,[184,181,187,501,176,174,176,176,174],0.888,[187,174,187,503,176,174,176,176,174],82.038,[187,178,187,505,176,174,176,176,174],95.504,[187,181,187,507,176,174,176,176,174],0.883,[229,174,187,509,176,174,176,176,174],90.624,[229,178,187,511,176,174,176,176,174],99.271,[229,181,187,513,176,174,176,176,174],0.948,[],[294],[],[],[519],"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":521,"group":522,"sourceId":523,"sourceLabel":524,"table":525,"selfRows":526,"metrics":527,"seqs":552,"entrants":561,"cells":571,"outcomes":770,"locators":772,"hardware":773,"wordings":775,"notes":776},"wang2025planarmesh-table-i","wang2025planarmesh:Table I","wang2025planarmesh","Wang et al., 2025a","Table I",27,[528,531,533,536,538,542,545,548,550],{"label":529,"unit":530,"statistic":42,"alignment":43},"Per-Scan Time (s)","s",{"label":532,"unit":120,"statistic":42,"alignment":43},"File size (MB), PLY binary",{"label":534,"unit":535,"statistic":42,"alignment":43},"Num of Faces","count",{"label":537,"unit":535,"statistic":42,"alignment":43},"Num of Vertices",{"label":539,"unit":540,"statistic":541,"alignment":43},"Mean distance to TLS ground truth (m)","m","mean",{"label":543,"unit":540,"statistic":544,"alignment":43},"Std of distance to TLS ground truth (m)","std",{"label":546,"unit":547,"statistic":42,"alignment":43},"Precision at 0.1 m","fraction",{"label":549,"unit":547,"statistic":42,"alignment":43},"Recall at 0.1 m",{"label":551,"unit":547,"statistic":42,"alignment":43},"F-Score at 0.1 m",[553,557,559],{"dataset":554,"sequence":555,"environment":556},"Oxford Spires","Christ Church 03 (about 307 m)","existing buildings, indoor and outdoor (walking survey)",{"dataset":554,"sequence":558,"environment":556},"Keble College 03 (about 108 m)",{"dataset":554,"sequence":560,"environment":556},"Observatory 01 (about 324 m)",[562,565,568,570],{"name":563,"methodId":564,"linkable":163,"proposed":70,"self":70},"VDBFusion","vizzo2022vdbfusion",{"name":566,"methodId":567,"linkable":163,"proposed":70,"self":70},"ImMesh","lin2023immesh",{"name":569,"methodId":523,"linkable":163,"proposed":163,"self":70},"PlanarMesh (Ours)",{"name":7,"methodId":5,"linkable":163,"proposed":70,"self":163},[572,574,576,578,580,582,584,586,588,590,592,594,596,598,600,602,604,605,607,609,611,613,615,617,619,621,623,625,627,629,630,631,633,635,636,638,640,642,644,646,648,650,652,654,656,657,659,661,663,665,667,669,670,671,673,675,677,679,681,683,685,687,689,691,693,695,696,697,698,700,701,702,704,706,708,710,712,714,716,718,719,720,722,724,726,728,730,732,734,736,737,739,741,743,745,747,749,751,753,755,757,759,760,761,763,765,767,769],[174,174,174,573,176,174,174,176,174],0.871,[174,178,174,575,176,174,176,176,174],53.6,[174,181,174,577,176,174,176,176,174],1992391,[174,184,174,579,176,174,176,176,174],1152788,[174,187,174,581,176,174,176,176,174],0.044,[174,229,174,583,176,174,176,176,174],0.077,[174,240,174,585,176,174,176,176,174],0.918,[174,251,174,587,176,174,176,176,174],0.97,[174,262,174,589,176,174,176,176,174],0.943,[178,174,174,591,176,174,174,176,174],0.724,[178,178,174,593,176,174,176,176,174],370.9,[178,181,174,595,176,174,176,176,174],21180823,[178,184,174,597,176,174,176,176,174],7959789,[178,187,174,599,176,174,176,176,174],0.09,[178,229,174,601,176,174,176,176,174],0.186,[178,240,174,603,176,174,176,176,174],0.82,[178,251,174,249,176,174,176,176,174],[178,262,174,606,176,174,176,176,174],0.897,[181,174,174,608,176,174,174,176,174],0.392,[181,178,174,610,176,174,176,176,174],10.1,[181,181,174,612,176,174,176,176,174],398712,[181,184,174,614,176,174,176,176,174],411907,[181,187,174,616,176,174,176,176,174],0.037,[181,229,174,618,176,174,176,176,174],0.081,[181,240,174,620,176,174,176,176,174],0.951,[181,251,174,622,176,174,176,176,174],0.964,[181,262,174,624,176,174,176,176,174],0.957,[184,174,174,626,176,174,174,176,174],0.432,[184,178,174,628,176,174,176,176,174],3.4,[184,181,174,64,174,174,176,176,174],[184,184,174,64,174,174,176,176,174],[184,187,174,632,176,174,176,176,174],0.04,[184,229,174,634,176,174,176,176,174],0.083,[184,240,174,589,176,174,176,176,174],[184,251,174,637,176,174,176,176,174],0.991,[184,262,174,639,176,174,176,176,174],0.966,[174,174,178,641,176,174,174,176,174],0.968,[174,178,178,643,176,174,176,176,174],51.3,[174,181,178,645,176,174,176,176,174],1821087,[174,184,178,647,176,174,176,176,174],1150250,[174,187,178,649,176,174,176,176,174],0.033,[174,229,178,651,176,174,176,176,174],0.113,[174,240,178,653,176,174,176,176,174],0.962,[174,251,178,655,176,174,176,176,174],0.94,[174,262,178,620,176,174,176,176,174],[178,174,178,658,176,174,174,176,174],0.355,[178,178,178,660,176,174,176,176,174],163.8,[178,181,178,662,176,174,176,176,174],9057110,[178,184,178,664,176,174,176,176,174],3838040,[178,187,178,666,176,174,176,176,174],0.035,[178,229,178,668,176,174,176,176,174],0.064,[178,240,178,369,176,174,176,176,174],[178,251,178,585,176,174,176,176,174],[178,262,178,672,176,174,176,176,174],0.936,[181,174,178,674,176,174,174,176,174],0.416,[181,178,178,676,176,174,176,176,174],7.3,[181,181,178,678,176,174,176,176,174],287020,[181,184,178,680,176,174,176,176,174],296254,[181,187,178,682,176,174,176,176,174],0.031,[181,229,178,684,176,174,176,176,174],0.134,[181,240,178,686,176,174,176,176,174],0.979,[181,251,178,688,176,174,176,176,174],0.894,[181,262,178,690,176,174,176,176,174],0.935,[184,174,178,692,176,174,174,176,174],0.328,[184,178,178,694,176,174,176,176,174],24.3,[184,181,178,64,174,174,176,176,174],[184,184,178,64,174,174,176,176,174],[184,187,178,632,176,174,176,176,174],[184,229,178,699,176,174,176,176,174],0.159,[184,240,178,622,176,174,176,176,174],[184,251,178,639,176,174,176,176,174],[184,262,178,703,176,174,176,176,174],0.965,[174,174,181,705,176,174,174,176,174],2.406,[174,178,181,707,176,174,176,176,174],148.5,[174,181,181,709,176,174,176,176,174],5246193,[174,184,181,711,176,174,176,176,174],3346024,[174,187,181,713,176,174,176,176,174],0.047,[174,229,181,715,176,174,176,176,174],0.104,[174,240,181,717,176,174,176,176,174],0.899,[174,251,181,717,176,174,176,176,174],[174,262,181,717,176,174,176,176,174],[178,174,181,721,176,174,174,176,174],0.448,[178,178,181,723,176,174,176,176,174],424.7,[178,181,181,725,176,174,176,176,174],23448665,[178,184,181,727,176,174,176,176,174],9986250,[178,187,181,729,176,174,176,176,174],0.056,[178,229,181,731,176,174,176,176,174],0.089,[178,240,181,733,176,174,176,176,174],0.878,[178,251,181,735,176,174,176,176,174],0.832,[178,262,181,447,176,174,176,176,174],[181,174,181,738,176,174,174,176,174],0.213,[181,178,181,740,176,174,176,176,174],15.3,[181,181,181,742,176,174,176,176,174],546415,[181,184,181,744,176,174,176,176,174],682725,[181,187,181,746,176,174,176,176,174],0.042,[181,229,181,748,176,174,176,176,174],0.114,[181,240,181,750,176,174,176,176,174],0.929,[181,251,181,752,176,174,176,176,174],0.847,[181,262,181,754,176,174,176,176,174],0.886,[184,174,181,756,176,174,174,176,174],0.659,[184,178,181,758,176,174,176,176,174],67.8,[184,181,181,64,174,174,176,176,174],[184,184,181,64,174,174,176,176,174],[184,187,181,762,176,174,176,176,174],0.055,[184,229,181,764,176,174,176,176,174],0.15,[184,240,181,766,176,174,176,176,174],0.896,[184,251,181,768,176,174,176,176,174],0.941,[184,262,181,585,176,174,176,176,174],[771],"not_applicable (N\u002FA: occupancy map has no faces or vertices)",[525],[774],"28-core Intel i7 CPU, no GPU; PlanarMesh uses all cores, baselines one core each (Sec. IV-A)",[],[777],"Oxford Spires; each method meshes individual scans with ground-truth poses (every undistorted scan registered to the TLS map); meshes sampled to the raw scan point count; distances to the TLS map after pre-filtering areas not seen by both; precision, recall and F-score at 0.1 m; OctoMap voxel 0.05 m, ImMesh and VDBFusion 0.1 m, baselines configured for about 1 Hz on one core, PlanarMesh on all 28 cores; file size as PLY binary; OctoMap has no faces or vertices (N\u002FA)",{"slug":779,"group":780,"sourceId":781,"sourceLabel":782,"table":525,"selfRows":783,"metrics":784,"seqs":791,"entrants":807,"cells":821,"outcomes":963,"locators":964,"hardware":965,"wordings":966,"notes":967},"dufomap2024-table-i","dufomap2024:Table I","dufomap2024","Duberg et al., 2024",12,[785,787,789],{"label":786,"unit":299,"statistic":42,"alignment":43},"SA (static accuracy, share of static points correctly kept)",{"label":788,"unit":299,"statistic":42,"alignment":43},"DA (dynamic accuracy, share of dynamic points correctly labelled)",{"label":790,"unit":299,"statistic":42,"alignment":43},"AA (associated accuracy, sqrt(SA x DA))",[792,796,799,803],{"dataset":793,"sequence":794,"environment":795},"KITTI (SemanticKITTI labels and poses)","00 small town","small town (HDL-64E)",{"dataset":793,"sequence":797,"environment":798},"01 highway","highway (HDL-64E)",{"dataset":800,"sequence":801,"environment":802},"Argoverse 2","big city","urban big city (two VLP-32C)",{"dataset":804,"sequence":805,"environment":806},"Semi-indoor (self-collected)","semi-indoor","highly structured semi-indoor area, sparse 16-channel LiDAR (VLP-16)",[808,810,812,814,816,819],{"name":809,"methodId":328,"linkable":163,"proposed":70,"self":70},"Removert [8]",{"name":811,"methodId":292,"linkable":163,"proposed":70,"self":70},"ERASOR [9]",{"name":813,"methodId":5,"linkable":163,"proposed":70,"self":163},"OctoMap [16]",{"name":815,"methodId":781,"linkable":163,"proposed":163,"self":70},"DUFOMap (Ours)",{"name":817,"methodId":818,"linkable":163,"proposed":70,"self":70},"Dynablox [17]","dynablox2023",{"name":820,"methodId":781,"linkable":163,"proposed":163,"self":70},"DUFOMap* (Ours, online)",[822,824,826,828,830,832,834,836,838,840,842,844,846,848,850,852,854,856,858,860,862,864,866,868,870,872,874,876,878,880,882,884,886,888,890,892,894,896,898,900,902,904,906,908,910,912,914,916,917,919,921,923,925,927,929,931,933,934,936,938,940,942,944,946,948,950,952,954,956,958,960,962],[174,174,174,823,176,174,176,176,174],99.44,[174,178,174,825,176,174,176,176,174],41.53,[174,181,174,827,176,174,176,176,174],64.26,[174,174,178,829,176,174,176,176,174],97.81,[174,178,178,831,176,174,176,176,174],39.56,[174,181,178,833,176,174,176,176,174],62.2,[174,174,181,835,176,174,176,176,174],98.97,[174,178,181,837,176,174,176,176,174],31.16,[174,181,181,839,176,174,176,176,174],55.53,[174,174,184,841,176,174,176,176,174],99.96,[174,178,184,843,176,174,176,176,174],12.15,[174,181,184,845,176,174,176,176,174],34.85,[178,174,174,847,176,174,176,176,174],66.7,[178,178,174,849,176,174,176,176,174],98.54,[178,181,174,851,176,174,176,176,174],81.07,[178,174,178,853,176,174,176,176,174],98.12,[178,178,178,855,176,174,176,176,174],90.94,[178,181,178,857,176,174,176,176,174],94.46,[178,174,181,859,176,174,176,176,174],77.51,[178,178,181,861,176,174,176,176,174],99.18,[178,181,181,863,176,174,176,176,174],87.68,[178,174,184,865,176,174,176,176,174],94.9,[178,178,184,867,176,174,176,176,174],66.26,[178,181,184,869,176,174,176,176,174],79.3,[181,174,174,871,176,174,176,176,174],68.05,[181,178,174,873,176,174,176,176,174],99.69,[181,181,174,875,176,174,176,176,174],82.37,[181,174,178,877,176,174,176,176,174],55.55,[181,178,178,879,176,174,176,176,174],99.59,[181,181,178,881,176,174,176,176,174],74.38,[181,174,181,883,176,174,176,176,174],69.04,[181,178,181,885,176,174,176,176,174],97.5,[181,181,181,887,176,174,176,176,174],82.04,[181,174,184,889,176,174,176,176,174],88.97,[181,178,184,891,176,174,176,176,174],82.18,[181,181,184,893,176,174,176,176,174],85.51,[184,174,174,895,176,174,176,176,174],97.96,[184,178,174,897,176,174,176,176,174],98.72,[184,181,174,899,176,174,176,176,174],98.34,[184,174,178,901,176,174,176,176,174],98.09,[184,178,178,903,176,174,176,176,174],94.2,[184,181,178,905,176,174,176,176,174],96.12,[184,174,181,907,176,174,176,176,174],96.67,[184,178,181,909,176,174,176,176,174],88.9,[184,181,181,911,176,174,176,176,174],92.7,[184,174,184,913,176,174,176,176,174],99.64,[184,178,184,915,176,174,176,176,174],83,[184,181,184,855,176,174,176,176,174],[187,174,174,918,176,174,176,176,174],96.76,[187,178,174,920,176,174,176,176,174],90.68,[187,181,174,922,176,174,176,176,174],93.67,[187,174,178,924,176,174,176,176,174],96.33,[187,178,178,926,176,174,176,176,174],68.01,[187,181,178,928,176,174,176,176,174],80.94,[187,174,181,930,176,174,176,176,174],96.08,[187,178,181,932,176,174,176,176,174],92.87,[187,181,181,857,176,174,176,176,174],[187,174,184,935,176,174,176,176,174],98.81,[187,178,184,937,176,174,176,176,174],36.49,[187,181,184,939,176,174,176,176,174],60.05,[229,174,174,941,176,174,176,176,174],98.37,[229,178,174,943,176,174,176,176,174],92.37,[229,181,174,945,176,174,176,176,174],95.31,[229,174,178,947,176,174,176,176,174],98.48,[229,178,178,949,176,174,176,176,174],81.34,[229,181,178,951,176,174,176,176,174],89.5,[229,174,181,953,176,174,176,176,174],98.66,[229,178,181,955,176,174,176,176,174],73.98,[229,181,181,957,176,174,176,176,174],85.43,[229,174,184,959,176,174,176,176,174],99.94,[229,178,184,961,176,174,176,176,174],54.76,[229,181,184,955,176,174,176,176,174],[],[525],[],[],[968],"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",[970,978,983,988,996,1002,1007,1012,1016,1022,1026,1031,1036,1040],{"group":971,"slug":972,"sourceLabel":973,"table":525,"selfRows":783,"datasets":974},"dynbench2023:Table I","dynbench2023-table-i","Zhang et al., 2023a",[975,976,977],"Argoverse 2.0","KITTI (SemanticKITTI labels)","Semi-indoor (authors' custom)",{"group":979,"slug":980,"sourceLabel":782,"table":981,"selfRows":273,"datasets":982},"dufomap2024:Table III","dufomap2024-table-iii","Table III",[976],{"group":984,"slug":985,"sourceLabel":6,"table":986,"selfRows":240,"datasets":987},"hornung2013octomap:Table 1","hornung2013octomap-table-1","Table 1",[79,92,88],{"group":989,"slug":990,"sourceLabel":991,"table":992,"selfRows":187,"datasets":993},"vizzo2022vdbfusion:Table 6","vizzo2022vdbfusion-table-6","Vizzo et al., 2022","Table 6",[994,995],"Cow and Lady","KITTI Odometry",{"group":997,"slug":998,"sourceLabel":999,"table":981,"selfRows":184,"datasets":1000},"supereight2018:Table III","supereight2018-table-iii","Vespa et al., 2018",[1001],"not_reported (map built by the SLAM system)",{"group":1003,"slug":1004,"sourceLabel":999,"table":1005,"selfRows":184,"datasets":1006},"supereight2018:Table IV","supereight2018-table-iv","Table IV",[1001],{"group":1008,"slug":1009,"sourceLabel":782,"table":294,"selfRows":181,"datasets":1010},"dufomap2024:Table II","dufomap2024-table-ii",[1011,804],"KITTI",{"group":1013,"slug":1014,"sourceLabel":973,"table":294,"selfRows":181,"datasets":1015},"dynbench2023:Table II","dynbench2023-table-ii",[42],{"group":1017,"slug":1018,"sourceLabel":999,"table":1019,"selfRows":181,"datasets":1020},"supereight2018:Text Sec.V-B","supereight2018-text-sec-v-b","Text Sec.V-B",[1021],"TUM RGB-D",{"group":1023,"slug":1024,"sourceLabel":991,"table":115,"selfRows":181,"datasets":1025},"vizzo2022vdbfusion:Table 2","vizzo2022vdbfusion-table-2",[994,995],{"group":1027,"slug":1028,"sourceLabel":991,"table":1029,"selfRows":181,"datasets":1030},"vizzo2022vdbfusion:Table 4","vizzo2022vdbfusion-table-4","Table 4",[994,995],{"group":1032,"slug":1033,"sourceLabel":991,"table":1034,"selfRows":181,"datasets":1035},"vizzo2022vdbfusion:Table 5","vizzo2022vdbfusion-table-5","Table 5",[994,995],{"group":1037,"slug":1038,"sourceLabel":293,"table":981,"selfRows":178,"datasets":1039},"erasor2021:Table III","erasor2021-table-iii",[307],{"group":1041,"slug":1042,"sourceLabel":6,"table":1043,"selfRows":178,"datasets":1044},"hornung2013octomap:Text Sec. 5.5.2","hornung2013octomap-text-sec-5-5-2","Text Sec. 5.5.2",[92],1790510659227]