[{"data":1,"prerenderedAt":953},["ShallowReactive",2],{"method-vizzo2021puma":3},{"method":4,"reference":54,"equipment":77,"figures":101,"results":102},{"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":27,"sensors":35,"platform":37,"estimator":39,"association":40,"timeModel":41,"deskew":42,"loopClosure":43,"globalOptimization":44,"mapRepresentation":45,"prior":44,"outputGeometry":46,"compute":47,"codeUrl":48,"codeLicense":49,"relatedVersions":50},"vizzo2021puma","Vizzo et al., 2021","PUMA","Poisson Surface Reconstruction for LiDAR Odometry and Mapping",2021,"recent","C12","odometry_with_local_mapping","PUMA 把最近 N 次掃描累積成局部點雲，以 Poisson 表面重建生成三角網格，並依頂點密度修剪 10% 低支持頂點，移除 Poisson 在無資料處外插的表面；新掃描以射線投射求與網格三角面的交點作為對應，進行點對面 ICP（frame-to-mesh）。局部網格每 M 次掃描併入全域網格，全域網格不參與估計，也未實作迴圈修正。","LiDAR odometry and mapping that builds sliding-window Poisson meshes, trims low-density vertices, and registers scans to the mesh with ray-casting-based point-to-plane ICP.","full_text_reviewed","peer_reviewed_published","main_body","僅在合成城市資料（Mai City）與 KITTI 驗證，未涉及營建；密度修剪會移除低觀測區域，對構件覆蓋完整度的影響未研究（推論）。",[20,21],"simulation","public_benchmark",[23,24,25,26],"On synthetic Mai City data with ground-truth poses (d = 0.03 m), the mesh map scored Chamfer 0.05, precision 93.28, recall 98.69 and F-score 95.91, versus TSDF and surfel maps built with the same poses (Table I).","Mesh maps need far less memory than point clouds or surfels and about the same as TSDF-extracted meshes on KITTI 04 and 07; final mesh sizes 72 MB and 306 MB (Sec. IV-C, Fig. 4).","Ray-casting association was more accurate and faster than mesh vertex sampling at all three mesh resolutions (Table III).","On KITTI 00-10 the ray-casting variant reached 1.55% average relative translational error and 0.74 deg\u002F100 m, versus 2.93% and 0.92 deg\u002F100 m for frame-to-frame SuMa under the same normal computation (Table II).",[28,29,30,31,32,33,34],"Meshing takes about 5 s per scan on CPU, making online operation infeasible (Sec. IV-F).","No loop closure; proposed as future work (Sec. V).","Watertight Poisson assumption requires density trimming for open outdoor scenes (Sec. III-B).","Ray-casting association does not handle large rotational motions properly and needs a good initial estimate (Sec. III-A).","Only horizontally mounted LiDARs are considered; upward-looking profiler scanners are not examined (Sec. III-B, footnote 1).","Range-image cross-product normals are sometimes less accurate than PCA normals (Sec. III-A), and the authors note that imposing them on all methods generally lowers the performance of normal-based metrics (Sec. IV-D).","SLAMesh authors report PUMA meshes are multi-layered and slightly warped at wall tops (Ruan et al. 2023, Sec. IV).",[36],"3D LiDAR",[38,20],"vehicle","iterative frame-to-mesh point-to-plane ICP with Huber kernel, initialised with the previous pose increment; during the first N = 30 scans, before a mesh exists, standard point-to-plane ICP is used (Sec. III-A, III-C, IV-D)","rays from the current sensor origin through every scan point are intersected with the local triangle mesh (Embree); the hit point and triangle normal form the correspondence, and pairs farther apart than 1 m are rejected (Sec. III-A, IV)","discrete poses","not_reported","none (listed as future work, Sec. V)","none","local Poisson surface reconstruction mesh (octree depth 10) rebuilt from the last N = 30 scans after every registered scan, with the 10% lowest-density vertices trimmed; global mesh aggregated every M = 30 scans with duplicate-triangle removal and used only for visualisation and output (Sec. III-B, III-C, IV)","global triangle mesh","CPU only (Intel Xeon W-2145, 8 cores at 3.70 GHz, 32 GB RAM; built on Open3D, Intel Embree for ray-triangle queries); per scan about 45 ms preprocessing and normals, 500 ms scan matching and about 5 s Poisson meshing, so online operation is infeasible (Sec. IV, IV-F)","https:\u002F\u002Fgithub.com\u002FPRBonn\u002Fpuma","MIT (repository LICENSE.txt)",[51],{"relation":52,"title":53,"doi_or_url":48},"code_release","PRBonn\u002Fpuma",{"id":5,"kind":55,"shortName":7,"title":8,"authors":56,"year":9,"venue":62,"venueType":63,"publisher":64,"volumeIssuePages":65,"doi":66,"arxivId":67,"url":68,"firstPublicDate":69,"publicationStatus":16,"metadataStatus":70,"fulltextStatus":15,"era":10,"classicReason":71,"codeUrl":48,"cluster":11,"topics":72,"mdpi":73,"verification":74,"label":6,"fulltextRoute":75,"versionRead":76,"addedByCensus":73},"method",[57,58,59,60,61],"Ignacio Vizzo","Xieyuanli Chen","Nived Chebrolu","Jens Behley","Cyrill Stachniss","2021 IEEE International Conference on Robotics and Automation (ICRA)","conference","IEEE","pp. 5624-5630","10.1109\u002Ficra48506.2021.9562069",null,"http:\u002F\u002Fwww.ipb.uni-bonn.de\u002Fwp-content\u002Fpapercite-data\u002Fpdf\u002Fvizzo2021icra.pdf","2021-05-30","metadata_verified","not_applicable",[11],false,"corrected","author copy","Author PDF from ipb.uni-bonn.de (7 pp. incl. references, pdfTeX, created 2021-03-25) read in full; the IEEE Xplore version of record (pp. 5624-5630) was not compared in this pass. PUMA's own KITTI RC row (Table II) is reproduced digit for digit in the Mesh-LOAM T-IV Table I, which supports the author PDF's table values",[78,84,91,96],{"category":79,"model":80,"canonical":80,"role":81,"dataset":67,"specs":82,"locator":83},"compute","Intel Xeon W-2145","compute for runtime","8 cores at 3.70 GHz, 32 GB RAM; algorithm runs entirely on CPU","Sec. IV",{"category":85,"model":86,"canonical":86,"role":87,"dataset":88,"specs":89,"locator":90},"lidar","rotating 3D LiDAR of the KITTI odometry benchmark (model not named in the paper)","dataset sensor","KITTI Odometry","real-world odometry, registration and memory experiments on KITTI sequences 00-10","Sec. IV-A; Tables II-III",{"category":85,"model":92,"canonical":92,"role":87,"dataset":93,"specs":94,"locator":95},"virtual 64-beam LiDAR sensor model (Mai City scans)","Mai City","synthetic scans of a CAD urban scene; a 64-beam sensor is implied by the statement that the ground-truth sampling used 320 beams instead of 64 (Sec. IV-A)","Sec. IV-A",{"category":85,"model":97,"canonical":97,"role":98,"dataset":93,"specs":99,"locator":100},"virtual 320-beam LiDAR sensor model (Mai City ground truth)","reference or ground truth","samples the CAD model with 320 beams to give a 62.5-million-point ground-truth cloud of the observable surfaces","Sec. IV-A; Fig. 3",[],{"totalRows":103,"groupCount":104,"groups":105,"others":861},128,22,[106,411,657,796],{"slug":107,"group":108,"sourceId":5,"sourceLabel":6,"table":109,"selfRows":110,"metrics":111,"seqs":119,"entrants":145,"cells":169,"outcomes":405,"locators":406,"hardware":407,"wordings":408,"notes":409},"vizzo2021puma-table-ii","vizzo2021puma:Table II","Table II",26,[112,116],{"label":113,"unit":114,"statistic":115,"alignment":44},"relative translational error (%)","%","mean",{"label":117,"unit":118,"statistic":115,"alignment":44},"relative rotational error (deg per 100 m)","deg\u002F100 m",[120,123,125,127,129,131,133,135,137,139,141,143],{"dataset":88,"sequence":121,"environment":122},"average of 00-10","outdoor driving (urban, country, highway)",{"dataset":88,"sequence":124,"environment":122},"00",{"dataset":88,"sequence":126,"environment":122},"01",{"dataset":88,"sequence":128,"environment":122},"02",{"dataset":88,"sequence":130,"environment":122},"03",{"dataset":88,"sequence":132,"environment":122},"04",{"dataset":88,"sequence":134,"environment":122},"05",{"dataset":88,"sequence":136,"environment":122},"06",{"dataset":88,"sequence":138,"environment":122},"07",{"dataset":88,"sequence":140,"environment":122},"08",{"dataset":88,"sequence":142,"environment":122},"09",{"dataset":88,"sequence":144,"environment":122},"10",[146,150,153,156,159,161,163,165,167],{"name":147,"methodId":148,"linkable":149,"proposed":73,"self":73},"point-to-point ICP [3] (map: None, DA: NN)","besl1992icp",true,{"name":151,"methodId":152,"linkable":73,"proposed":73,"self":73},"point-to-plane ICP [32] (map: None, DA: NN)","rusinkiewicz2001variants",{"name":154,"methodId":155,"linkable":149,"proposed":73,"self":73},"GICP [33] (map: None, DA: NN)","segal2009gicp",{"name":157,"methodId":158,"linkable":149,"proposed":73,"self":73},"SuMa [1] (map: None, DA: Proj.)","suma2018",{"name":160,"methodId":148,"linkable":149,"proposed":73,"self":73},"point-to-point ICP [3] (map: Point cloud, DA: NN)",{"name":162,"methodId":152,"linkable":73,"proposed":73,"self":73},"point-to-plane ICP [32] (map: Point cloud, DA: NN)",{"name":164,"methodId":155,"linkable":149,"proposed":73,"self":73},"GICP [33] (map: Point cloud, DA: NN)",{"name":166,"methodId":5,"linkable":149,"proposed":73,"self":149},"Ours (Δtree = 10) (map: Mesh, DA: NN)",{"name":168,"methodId":5,"linkable":149,"proposed":149,"self":149},"Ours (Δtree = 10) (map: Mesh, DA: RC)",[170,174,177,180,183,186,189,192,195,198,201,203,206,208,210,212,213,215,217,219,221,223,225,227,229,231,233,235,237,239,241,242,244,246,248,250,252,254,255,257,259,261,263,265,267,269,271,273,275,277,279,281,283,285,287,289,291,292,294,295,297,299,301,302,304,306,308,310,312,314,316,318,320,322,324,326,328,330,332,334,336,338,340,342,344,346,348,350,352,354,356,358,360,362,364,366,368,369,370,372,374,376,378,379,381,383,384,385,387,389,391,393,395,397,399,401,403],[171,171,171,172,173,171,173,173,171],0,4.86,-1,[171,171,175,176,173,171,173,173,171],1,3.65,[171,171,178,179,173,171,173,173,171],2,15.4,[171,171,181,182,173,171,173,173,171],3,4.6,[171,171,184,185,173,171,173,173,171],4,5.74,[171,171,187,188,173,171,173,173,171],5,2.44,[171,171,190,191,173,171,173,173,171],6,3.38,[171,171,193,194,173,171,173,173,171],7,2.94,[171,171,196,197,173,171,173,173,171],8,4.92,[171,171,199,200,173,171,173,173,171],9,3.75,[171,171,202,191,173,171,173,173,171],10,[171,171,204,205,173,171,173,173,171],11,3.24,[171,175,171,207,173,171,173,173,171],1.71,[175,171,171,209,173,171,173,173,171],7.6,[175,171,175,211,173,171,173,173,171],9.12,[175,171,178,202,173,171,173,173,171],[175,171,181,214,173,171,173,173,171],6.19,[175,171,184,216,173,171,173,173,171],6.04,[175,171,187,218,173,171,173,173,171],4.51,[175,171,190,220,173,171,173,173,171],7.69,[175,171,193,222,173,171,173,173,171],8.24,[175,171,196,224,173,171,173,173,171],5.53,[175,171,199,226,173,171,173,173,171],8.7,[175,171,202,228,173,171,173,173,171],9.37,[175,171,204,230,173,171,173,173,171],8.18,[175,175,171,232,173,171,173,173,171],3.49,[178,171,171,234,173,171,173,173,171],14.35,[178,171,175,236,173,171,173,173,171],7.35,[178,171,178,238,173,171,173,173,171],73.1,[178,171,181,240,173,171,173,173,171],14.4,[178,171,184,228,173,171,173,173,171],[178,171,187,243,173,171,173,173,171],13.6,[178,171,190,245,173,171,173,173,171],5.23,[178,171,193,247,173,171,173,173,171],2.23,[178,171,196,249,173,171,173,173,171],6.4,[178,171,199,251,173,171,173,173,171],7.27,[178,171,202,253,173,171,173,173,171],10.9,[178,171,204,196,173,171,173,173,171],[178,175,171,256,173,171,173,173,171],4.78,[181,171,171,258,173,171,173,173,171],2.93,[181,171,175,260,173,171,173,173,171],2.09,[181,171,178,262,173,171,173,173,171],4.05,[181,171,181,264,173,171,173,173,171],2.3,[181,171,184,266,173,171,173,173,171],1.43,[181,171,187,268,173,171,173,173,171],11.9,[181,171,190,270,173,171,173,173,171],1.46,[181,171,193,272,173,171,173,173,171],0.95,[181,171,196,274,173,171,173,173,171],1.75,[181,171,199,276,173,171,173,173,171],2.53,[181,171,202,278,173,171,173,173,171],1.92,[181,171,204,280,173,171,173,173,171],1.81,[181,175,171,282,173,171,173,173,171],0.92,[184,171,171,284,173,171,173,173,171],29.98,[184,171,175,286,173,171,173,173,171],9.25,[184,171,178,288,173,171,173,173,171],93.2,[184,171,181,290,173,171,173,173,171],30.4,[184,171,184,253,173,171,173,173,171],[184,171,187,293,173,171,173,173,171],91.4,[184,171,190,282,173,171,173,173,171],[184,171,193,296,173,171,173,173,171],33.9,[184,171,196,298,173,171,173,173,171],8.35,[184,171,199,300,173,171,173,173,171],2.81,[184,171,202,296,173,171,173,173,171],[184,171,204,303,173,171,173,173,171],14.8,[184,175,171,305,173,171,173,173,171],2.61,[187,171,171,307,173,171,173,173,171],18.92,[187,171,175,309,173,171,173,173,171],9.99,[187,171,178,311,173,171,173,173,171],77.1,[187,171,181,313,173,171,173,173,171],11.7,[187,171,184,315,173,171,173,173,171],2.31,[187,171,187,317,173,171,173,173,171],70,[187,171,190,319,173,171,173,173,171],2.62,[187,171,193,321,173,171,173,173,171],1.84,[187,171,196,323,173,171,173,173,171],1.79,[187,171,199,325,173,171,173,173,171],3.67,[187,171,202,327,173,171,173,173,171],17.4,[187,171,204,329,173,171,173,173,171],9.7,[187,175,171,331,173,171,173,173,171],4.01,[190,171,171,333,173,171,173,173,171],20.43,[190,171,175,335,173,171,173,173,171],4.34,[190,171,178,337,173,171,173,173,171],93.1,[190,171,181,339,173,171,173,173,171],10.7,[190,171,184,341,173,171,173,173,171],2.21,[190,171,187,343,173,171,173,173,171],83.7,[190,171,190,345,173,171,173,173,171],1.56,[190,171,193,347,173,171,173,173,171],1.42,[190,171,196,349,173,171,173,173,171],1.19,[190,171,199,351,173,171,173,173,171],2.33,[190,171,202,353,173,171,173,173,171],21.8,[190,171,204,355,173,171,173,173,171],2.37,[190,175,171,357,173,171,173,173,171],2.76,[193,171,171,359,173,171,173,173,171],2.15,[193,171,175,361,173,171,173,173,171],3.14,[193,171,178,363,173,171,173,173,171],4.32,[193,171,181,365,173,171,173,173,171],1.91,[193,171,184,367,173,171,173,173,171],1.34,[193,171,187,260,173,171,173,173,171],[193,171,190,345,173,171,173,173,171],[193,171,193,371,173,171,173,173,171],1.41,[193,171,196,373,173,171,173,173,171],1.88,[193,171,199,375,173,171,173,173,171],1.97,[193,171,202,377,173,171,173,173,171],1.8,[193,171,204,341,173,171,173,173,171],[193,175,171,380,173,171,173,173,171],1.14,[196,171,171,382,173,171,173,173,171],1.55,[196,171,175,270,173,171,173,173,171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odometry training sequences 00-10; relative errors averaged over 100-800 m segments; all methods share the range-image normals and Huber loss; Map None = frame-to-frame, Map Point cloud = frame-to-model on the last N scans; DA = data association (NN nearest neighbour, Proj. projective, RC ray casting). Per-sequence rotational errors omitted to respect the row cap; only the rotational average is kept",{"slug":412,"group":413,"sourceId":414,"sourceLabel":415,"table":109,"selfRows":416,"metrics":417,"seqs":423,"entrants":439,"cells":461,"outcomes":649,"locators":651,"hardware":652,"wordings":653,"notes":654},"ruan2023slamesh-table-ii","ruan2023slamesh:Table II","ruan2023slamesh","Ruan et al., 2023",13,[418,420],{"label":419,"unit":114,"statistic":115,"alignment":42},"relative translation error (%)",{"label":421,"unit":422,"statistic":115,"alignment":42},"relative rotation error (deg\u002F100m)","deg\u002F100m",[424,427,428,429,430,431,432,433,434,435,436,437],{"dataset":425,"sequence":124,"environment":426},"KITTI odometry","urban, country and highway driving",{"dataset":425,"sequence":126,"environment":426},{"dataset":425,"sequence":128,"environment":426},{"dataset":425,"sequence":130,"environment":426},{"dataset":425,"sequence":132,"environment":426},{"dataset":425,"sequence":134,"environment":426},{"dataset":425,"sequence":136,"environment":426},{"dataset":425,"sequence":138,"environment":426},{"dataset":425,"sequence":140,"environment":426},{"dataset":425,"sequence":142,"environment":426},{"dataset":425,"sequence":144,"environment":426},{"dataset":425,"sequence":438,"environment":426},"Mean",[440,443,446,448,451,453,455,457,459],{"name":441,"methodId":442,"linkable":149,"proposed":73,"self":73},"LOAM","loam2014",{"name":444,"methodId":445,"linkable":149,"proposed":73,"self":73},"A-LOAM","aloam_software",{"name":447,"methodId":158,"linkable":149,"proposed":73,"self":73},"Suma",{"name":449,"methodId":450,"linkable":149,"proposed":73,"self":73},"Suma++","sumapp2019",{"name":452,"methodId":67,"linkable":73,"proposed":73,"self":73},"Litamin2",{"name":454,"methodId":5,"linkable":149,"proposed":73,"self":149},"Puma",{"name":456,"methodId":414,"linkable":149,"proposed":149,"self":73},"SLAMesh (Ours) Full",{"name":458,"methodId":414,"linkable":149,"proposed":149,"self":73},"SLAMesh w\u002Fo Comb.",{"name":460,"methodId":414,"linkable":149,"proposed":149,"self":73},"SLAMesh w\u002Fo 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171,190,537,173,171,173,173,171],[196,171,193,640,173,171,173,173,171],0.43,[196,171,196,380,173,171,173,173,171],[196,171,199,479,173,171,173,173,171],[196,171,202,644,173,171,173,173,171],0.93,[196,171,204,646,173,171,173,173,171],0.756,[196,175,204,648,173,171,173,173,175],0.366,[650],"not_reported (dash in table)",[109],[],[],[655,656],"KITTI odometry 00-10, relative translation error; baseline values imported from their published papers (not rerun); SLAMesh without loop closure; w\u002Fo Comb. and w\u002Fo P2Mesh are ablations","KITTI odometry 00-10, relative translation error; baseline values imported from their published papers (not rerun); SLAMesh without loop closure; w\u002Fo Comb. and w\u002Fo P2Mesh are ablations; only the Mean column of the rotation row is extracted",{"slug":658,"group":659,"sourceId":660,"sourceLabel":661,"table":662,"selfRows":202,"metrics":663,"seqs":677,"entrants":683,"cells":696,"outcomes":789,"locators":790,"hardware":792,"wordings":793,"notes":794},"zhu2025meshloam-table-v","zhu2025meshloam:Table V","zhu2025meshloam","Zhu et al., 2025","Table V",[664,667,669,671,673,675],{"label":665,"unit":666,"statistic":42,"alignment":44},"Comp. (cm), completion","cm",{"label":668,"unit":666,"statistic":42,"alignment":44},"Acc. (cm), accuracy",{"label":670,"unit":666,"statistic":42,"alignment":44},"C-L1 (cm), Chamfer-L1 distance",{"label":672,"unit":114,"statistic":42,"alignment":44},"Comp.Ratio (%)",{"label":674,"unit":114,"statistic":42,"alignment":44},"F-score (10cm) (%)",{"label":676,"unit":114,"statistic":42,"alignment":44},"F-score (20cm) (%)",[678,680],{"dataset":93,"sequence":93,"environment":679},"simulated urban street",{"dataset":681,"sequence":42,"environment":682},"Newer College","outdoor college (handheld)",[684,687,689,692,694],{"name":685,"methodId":686,"linkable":149,"proposed":73,"self":73},"VDB Fusion [28]","vizzo2022vdbfusion",{"name":688,"methodId":5,"linkable":149,"proposed":73,"self":149},"Puma [13]",{"name":690,"methodId":691,"linkable":149,"proposed":73,"self":73},"SHINE-Mapping [30]","shinemapping2023",{"name":693,"methodId":414,"linkable":149,"proposed":73,"self":73},"SLAMesh [14]",{"name":695,"methodId":660,"linkable":149,"proposed":149,"self":73},"Ours",[697,699,701,703,705,707,709,710,712,714,716,718,719,721,723,725,726,728,730,732,734,736,737,739,741,743,744,746,748,750,752,754,756,758,760,762,764,766,768,770,772,774,775,777,779,781,783,784,786,788],[171,171,171,698,173,171,173,173,171],6.9,[171,175,171,700,173,171,173,173,171],1.3,[171,178,171,702,173,171,173,173,171],4.5,[171,181,171,704,173,171,173,173,171],90.2,[171,184,171,706,173,171,173,173,171],94.1,[171,171,175,708,173,171,173,173,171],12,[171,175,175,698,173,171,173,173,171],[171,178,175,711,173,171,173,173,171],9.4,[171,181,175,713,173,171,173,173,171],91.3,[171,187,175,715,173,171,173,173,171],92.6,[175,171,171,717,173,171,173,173,171],32,[175,175,171,392,173,171,173,173,171],[175,178,171,720,173,171,173,173,171],16.9,[175,181,171,722,173,171,173,173,171],78.8,[175,184,171,724,173,171,173,173,171],87.3,[175,171,175,179,173,171,173,173,171],[175,175,175,727,173,171,173,173,171],7.7,[175,178,175,729,173,171,173,173,171],11.5,[175,181,175,731,173,171,173,173,171],89.9,[175,187,175,733,173,171,173,173,171],91.9,[178,171,171,735,173,171,173,173,171],3.2,[178,175,171,515,173,171,173,173,171],[178,178,171,738,173,171,173,173,171],2.9,[178,181,171,740,173,171,173,173,171],95.2,[178,184,171,742,173,171,173,173,171],95.9,[178,171,175,202,173,171,173,173,171],[178,175,175,745,173,171,173,173,171],6.7,[178,178,175,747,173,171,173,173,171],8.4,[178,181,175,749,173,171,173,173,171],93.6,[178,187,175,751,173,171,173,173,171],93.7,[181,171,171,753,173,171,173,173,171],7.5,[181,175,171,755,173,171,173,173,171],3.7,[181,178,171,757,173,171,173,173,171],6.1,[181,181,171,759,173,171,173,173,171],89.2,[181,184,171,761,173,171,173,173,171],90.6,[181,171,175,763,173,171,173,173,171],13.7,[181,175,175,765,173,171,173,173,171],11.4,[181,178,175,767,173,171,173,173,171],12.6,[181,181,175,769,173,171,173,173,171],83.5,[181,187,175,771,173,171,173,173,171],82.3,[184,171,171,773,173,171,173,173,171],2.5,[184,175,171,392,173,171,173,173,171],[184,178,171,776,173,171,173,173,171],2.4,[184,181,171,778,173,171,173,173,171],96.3,[184,184,171,780,173,171,173,173,171],97.4,[184,171,175,782,173,171,173,173,171],9.6,[184,175,175,745,173,171,173,173,171],[184,178,175,785,173,171,173,173,171],8.2,[184,181,175,787,173,171,173,173,171],94.2,[184,187,175,706,173,171,173,173,171],[],[791],"Table V (VoR)",[],[],[795],"Mesh quality with ground-truth poses for all methods, voxel size 0.1 m, settings of SHINE-Mapping; distances in cm; completion ratio and F-score in % at 10 cm (Mai City) and 20 cm (Newer College)",{"slug":797,"group":798,"sourceId":5,"sourceLabel":6,"table":799,"selfRows":199,"metrics":800,"seqs":808,"entrants":816,"cells":821,"outcomes":854,"locators":855,"hardware":856,"wordings":858,"notes":859},"vizzo2021puma-table-iii","vizzo2021puma:Table III","Table III",[801,803,805],{"label":802,"unit":114,"statistic":115,"alignment":44},"terr, relative translational error (%)",{"label":804,"unit":118,"statistic":115,"alignment":44},"rerr, relative rotational error (deg per 100 m)",{"label":806,"unit":807,"statistic":42,"alignment":44},"runtime (ms), registration","ms",[809,812,814],{"dataset":88,"sequence":810,"environment":811},"KITTI training sequences 00-10; Δtree = 8 (30K vertices)","outdoor driving",{"dataset":88,"sequence":813,"environment":811},"KITTI training sequences 00-10; Δtree = 9 (100K vertices)",{"dataset":88,"sequence":815,"environment":811},"KITTI training sequences 00-10; Δtree = 10 (300K vertices)",[817,819],{"name":818,"methodId":67,"linkable":73,"proposed":73,"self":73},"Mesh vertex-sampling (point-to-plane ICP on sampled mesh vertices, nearest neighbours)",{"name":820,"methodId":5,"linkable":149,"proposed":149,"self":149},"Mesh ray-casting (proposed)",[822,824,826,828,830,832,834,836,838,840,842,843,845,847,848,850,851,852],[171,171,171,823,173,171,173,173,171],2.82,[171,175,171,825,173,171,173,173,171],1.23,[171,178,171,827,173,171,171,173,171],682,[175,171,171,829,173,171,173,173,171],1.73,[175,175,171,831,173,171,173,173,171],0.9,[175,178,171,833,173,171,171,173,171],395,[171,171,175,835,173,171,173,173,171],2.05,[171,175,175,837,173,171,173,173,171],1.08,[171,178,175,839,173,171,171,173,171],645,[175,171,175,841,173,171,173,173,171],1.53,[175,175,175,404,173,171,173,173,171],[175,178,175,844,173,171,171,173,171],418,[171,171,178,846,173,171,173,173,171],2.14,[171,175,178,501,173,171,173,173,171],[171,178,178,849,173,171,171,173,171],789,[175,171,178,345,173,171,173,173,171],[175,175,178,404,173,171,173,173,171],[175,178,178,853,173,171,171,173,171],535,[],[799],[857],"Intel Xeon W-2145, 8 cores at 3.70 GHz, 32 GB RAM, CPU only (Sec. IV)",[],[860],"Registration of every scan to the local mesh on the full KITTI training sequences; Mesh vertex-sampling = standard point-to-plane ICP on sampled mesh vertices (nearest neighbours) versus Mesh ray-casting (proposed); Poisson octree depth 8, 9, 10 give about 30K, 100K, 300K vertices; relative errors over 100-800 m",[862,869,874,880,885,890,896,900,905,910,914,919,924,928,933,938,944,949],{"group":863,"slug":864,"sourceLabel":865,"table":866,"selfRows":196,"datasets":867},"nerfloam2023:Table 1","nerfloam2023-table-1","Deng et al., 2023","Table 1",[868,681],"MaiCity",{"group":870,"slug":871,"sourceLabel":865,"table":872,"selfRows":196,"datasets":873},"nerfloam2023:Table 5","nerfloam2023-table-5","Table 5",[425],{"group":875,"slug":876,"sourceLabel":877,"table":878,"selfRows":196,"datasets":879},"pinslam2024:Table XI","pinslam2024-table-xi","Pan et al., 2024","Table XI",[681],{"group":881,"slug":882,"sourceLabel":661,"table":799,"selfRows":190,"datasets":883},"zhu2025meshloam:Table III","zhu2025meshloam-table-iii",[884],"Hilti SLAM Challenge 2021",{"group":886,"slug":887,"sourceLabel":661,"table":888,"selfRows":190,"datasets":889},"zhu2025meshloam:Table VIII","zhu2025meshloam-table-viii","Table VIII",[88],{"group":891,"slug":892,"sourceLabel":893,"table":109,"selfRows":187,"datasets":894},"shinemapping2023:Table II","shinemapping2023-table-ii","Zhong et al., 2023",[895],"MaiCity (synthetic)",{"group":897,"slug":898,"sourceLabel":893,"table":799,"selfRows":187,"datasets":899},"shinemapping2023:Table III","shinemapping2023-table-iii",[681],{"group":901,"slug":902,"sourceLabel":865,"table":903,"selfRows":184,"datasets":904},"nerfloam2023:Table 3","nerfloam2023-table-3","Table 3",[425,868,681],{"group":906,"slug":907,"sourceLabel":6,"table":908,"selfRows":184,"datasets":909},"vizzo2021puma:Table I","vizzo2021puma-table-i","Table I",[93],{"group":911,"slug":912,"sourceLabel":415,"table":908,"selfRows":181,"datasets":913},"ruan2023slamesh:Table I","ruan2023slamesh-table-i",[93],{"group":915,"slug":916,"sourceLabel":6,"table":917,"selfRows":181,"datasets":918},"vizzo2021puma:Text Sec.IV-F","vizzo2021puma-text-sec-iv-f","Text Sec.IV-F",[42],{"group":920,"slug":921,"sourceLabel":6,"table":922,"selfRows":178,"datasets":923},"vizzo2021puma:Text Sec.IV-C (Fig. 4 labels)","vizzo2021puma-text-sec-iv-c-fig-4-labels","Text Sec.IV-C (Fig. 4 labels)",[88],{"group":925,"slug":926,"sourceLabel":661,"table":908,"selfRows":178,"datasets":927},"zhu2025meshloam:Table I","zhu2025meshloam-table-i",[88],{"group":929,"slug":930,"sourceLabel":661,"table":931,"selfRows":178,"datasets":932},"zhu2025meshloam:Table IV","zhu2025meshloam-table-iv","Table IV",[93,681],{"group":934,"slug":935,"sourceLabel":415,"table":936,"selfRows":175,"datasets":937},"ruan2023slamesh:Text Sec. IV-D","ruan2023slamesh-text-sec-iv-d","Text Sec. IV-D",[425],{"group":939,"slug":940,"sourceLabel":941,"table":942,"selfRows":175,"datasets":943},"zhang2024_3dlidarslam_survey:Table 8","zhang2024-3dlidarslam-survey-table-8","Zhang et al., 2024a","Table 8",[425],{"group":945,"slug":946,"sourceLabel":941,"table":947,"selfRows":175,"datasets":948},"zhang2024_3dlidarslam_survey:Table 9","zhang2024-3dlidarslam-survey-table-9","Table 9",[425],{"group":950,"slug":951,"sourceLabel":661,"table":109,"selfRows":175,"datasets":952},"zhu2025meshloam:Table II","zhu2025meshloam-table-ii",[88],1790510663336]