[{"data":1,"prerenderedAt":1036},["ShallowReactive",2],{"method-lin2023immesh":3},{"method":4,"reference":58,"equipment":83,"figures":144,"results":182},{"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":23,"limitations":29,"sensors":35,"platform":38,"estimator":42,"association":43,"timeModel":44,"deskew":45,"loopClosure":46,"globalOptimization":47,"mapRepresentation":48,"prior":47,"outputGeometry":49,"compute":50,"codeUrl":51,"codeLicense":52,"relatedVersions":53},"lin2023immesh","Lin et al., 2023","ImMesh","ImMesh: An Immediate LiDAR Localization and Meshing Framework",2023,"recent","C12","odometry_with_local_mapping","ImMesh 以 VoxelMap 的機率平面與迭代卡爾曼濾波估計位姿，並把經空間降採樣、配準後的 LiDAR 點當成網格頂點（以 ikd-Tree 維持頂點最小間距）；每個有新點的體素將其頂點投影到該體素主平面上，以二維 Delaunay 三角化建立三角面，再以類似 git 的 pull、commit、push 步驟增量合併到全域網格。整體在一般桌上型 CPU 上即時執行。","Uses VoxelMap-based localization and treats registered LiDAR points as vertices, meshing each active voxel by projecting to its dominant plane and applying 2D Delaunay triangulation with incremental pull\u002Fcommit\u002Fpush updates.","full_text_reviewed","peer_reviewed_published","main_body","以校園、城市、公開資料集與 AirSim 模擬驗證，未涉及營建；作者指出固定體素尺寸無法重建小於體素的細節，對小型構件與設備量測是限制（推論）。",[20,21,22],"simulation","public_benchmark","controlled_experiment",[24,25,26,27,28],"Real-time meshing on a CPU across KITTI, NCLT, NTU VIRAL and R3LIVE datasets with bounded per-scan time (Sec. VIII-B, Tables II and IV).","In quantitative tests with ground-truth poses, triangle quality ranked second only to offline Delaunay\u002Fgraph-cut and above TSDF and Poisson (Sec. VIII-C4).","Handheld trials closed loops after 957 m and 391 m without loop detection (Sec. VIII-A).","On three Complex Urban sequences (3.06 to 11.72 km) ImMesh needed 3 to 6 min against about 5 to 10 h for Poisson and scored higher recall, precision and F-score (Table V).","In the UAV texture application a 975 m, 325 s flight was meshed and textured in 686 s in total (328 s ImMesh, 330 s R3LIVE++, 28 s texturing) (Sec. VIII-E).",[30,31,32,33,34],"Lacks scalability in spatial resolution: large planes get many small facets and objects smaller than a voxel are poorly reconstructed (Sec. IX).","No loop correction, so drift can make revisited areas inconsistent (Sec. IX).","TSDF baseline could not run on over-3 km sequences due to GPU memory; Delaunay baseline failed on Complex Urban data (Sec. VIII-C4).","Textured UAV mesh shows isolated facets where scans are missing and blurry textures at large viewing angles (Sec. VIII-E).","(reviewer check) Table V reports Poisson completeness 0.0070 m and accuracy 0.0059 m on Urban03, better than ImMesh, which conflicts with the text; the stated 0.93% to 1.06% runtime ratio does not match Urban02 (about 0.86%).",[36,37],"3D LiDAR (spinning and solid-state)","IMU (optional)",[39,40,41,20],"handheld","vehicle","UAV","iterated Kalman filter with probabilistic planes (built on VoxelMap)","point-to-plane registration against voxel plane features (VoxelMap)","discrete poses","in-frame motion distortion compensated by IMU backward propagation (method of FAST-LIO) before registration (Sec. V-A)","none (stated limitation, Sec. IX)","none","spatially downsampled, registered LiDAR points kept as mesh vertices with a minimum spacing (0.15 m for mechanical, 0.10 m for solid-state LiDAR) enforced by an ikd-Tree, stored in hashed voxels (0.60 m or 0.40 m) and hashed regions (15 m or 10 m); triangle facets stored per region and indexed in a facet hash table","triangle mesh published at scan rate; also point cloud reinforcement via mesh rasterization","real-time on CPU (handheld mini-computer, Intel i9-10900, 64 GB RAM); per-dataset mean per-scan time 9.8 to 31.3 ms for meshing and 11.9 to 42.2 ms for localization at 10 Hz input (Table IV); the Experiment-3 comparison ran on an Intel i7-9700K with 64 GB RAM, where only the TSDF baseline used the Nvidia 2080 Ti GPU","https:\u002F\u002Fgithub.com\u002Fhku-mars\u002FImMesh","GPL-2.0, free for personal and academic use; commercial use requires contacting the authors (per README)",[54],{"relation":55,"title":56,"doi_or_url":57},"preprint","arXiv:2301.05206 (v1 2023-01-12, up to v3)","https:\u002F\u002Farxiv.org\u002Fabs\u002F2301.05206",{"id":5,"kind":59,"shortName":7,"title":8,"authors":60,"year":9,"venue":69,"venueType":70,"publisher":71,"volumeIssuePages":72,"doi":73,"arxivId":74,"url":57,"firstPublicDate":75,"publicationStatus":16,"metadataStatus":76,"fulltextStatus":15,"era":10,"classicReason":77,"codeUrl":51,"cluster":11,"topics":78,"mdpi":79,"verification":80,"label":6,"fulltextRoute":81,"versionRead":82,"addedByCensus":79},"method",[61,62,63,64,65,66,67,68],"Jiarong Lin","Chongjian Yuan","Yixi Cai","Haotian Li","Yunfan Ren","Yuying Zou","Xiaoping Hong","Fu Zhang","IEEE Transactions on Robotics","journal","IEEE","39(6):4312-4331","10.1109\u002Ftro.2023.3321227","2301.05206","2023-01-12","metadata_verified","not_applicable",[11],false,"corrected","arXiv","arXiv v3 (2023-11-11) read in full; IEEE T-RO version of record 39(6):4312-4331 obtained via NTU access and its Sec. VII-VIII text compared (matches v3); the VoR tables are embedded images, so table values were transcribed from arXiv v3",[84,91,96,102,108,112,117,120,126,130,133,137,139],{"category":85,"model":86,"canonical":86,"role":87,"dataset":88,"specs":89,"locator":90},"platform","handheld data-collection device (custom rig, Fig. 6a)","method input",null,"mini-computer, Livox Avia LiDAR and a preview RGB camera","Sec. VIII-A1, Fig. 6",{"category":92,"model":93,"canonical":93,"role":87,"dataset":88,"specs":94,"locator":95},"lidar","Livox Avia","solid-state, Risley prism; FoV 70.4 x 77.2 deg; 240,000 points\u002Fs single return; USD 1,599 (Table I)","Sec. VIII-A1, Table I, Fig. 6",{"category":97,"model":98,"canonical":98,"role":99,"dataset":88,"specs":100,"locator":101},"compute","Intel i9-10900","compute for runtime","mini-computer CPU with 64 GB RAM","Sec. VIII-A1",{"category":92,"model":103,"canonical":103,"role":104,"dataset":105,"specs":106,"locator":107},"Velodyne HDL-64E","dataset sensor","KITTI","mechanical spinning 64-line; FoV 360.0 x 26.8 deg; 1,333,312 points\u002Fs; USD 75,000","Table I",{"category":92,"model":109,"canonical":109,"role":104,"dataset":110,"specs":111,"locator":107},"Velodyne HDL-32E","NCLT","mechanical spinning 32-line; FoV 360.0 x 41.3 deg; 695,000 points\u002Fs; USD 8,800",{"category":92,"model":113,"canonical":114,"role":104,"dataset":115,"specs":116,"locator":107},"Ouster OS1-16 Gen1","Ouster OS1-16","NTU VIRAL","mechanical spinning 16-line; FoV 360.0 x 33.2 deg; 327,680 points\u002Fs; USD 3,500",{"category":92,"model":93,"canonical":93,"role":104,"dataset":118,"specs":119,"locator":107},"R3LIVE dataset","solid-state, Risley prism; FoV 70.4 x 77.2 deg; 240,000 points\u002Fs",{"category":121,"model":122,"canonical":122,"role":104,"dataset":123,"specs":124,"locator":125},"other","Microsoft AirSim simulated depth camera","AirSim synthetic (Urban city, Cluttered field)","depth images with FoV 120 x 80 deg at 640x480, 320x240 and 160x120, unprojected to simulate LiDAR","Sec. VIII-C2, Fig. 7",{"category":97,"model":127,"canonical":127,"role":99,"dataset":88,"specs":128,"locator":129},"Intel i7-9700K","desktop CPU with 64 GB RAM","Sec. VIII-C3",{"category":97,"model":131,"canonical":131,"role":99,"dataset":88,"specs":132,"locator":129},"Nvidia 2080 Ti","12 GB graphics memory; used only by the TSDF baseline",{"category":85,"model":134,"canonical":134,"role":87,"dataset":88,"specs":135,"locator":136},"DJI M300","drone carrying Livox Avia and Hikvision camera","Sec. VIII-E, Fig. 11",{"category":92,"model":93,"canonical":93,"role":87,"dataset":88,"specs":138,"locator":136},"not_reported beyond model",{"category":140,"model":141,"canonical":141,"role":87,"dataset":88,"specs":142,"locator":143},"camera","Hikvision CA-050-11UC","global shutter RGB camera; images used for mesh texturing with R3LIVE++ poses","Sec. VIII-E",[145,158,166,174],{"refId":5,"refLabel":6,"fig":146,"whatZh":147,"license":148,"licenseUrl":149,"sourceUrl":150,"src":151,"width":152,"height":153,"thumb":154,"thumbWidth":155,"thumbHeight":156,"modified":157},"Fig. 1","ImMesh 線上重建的三角網格與估計位姿，並以 R3LIVE 相機位姿貼上影像紋理的結果","CC BY 4.0 (arXiv v3)","https:\u002F\u002Fcreativecommons.org\u002Flicenses\u002Fby\u002F4.0\u002F","https:\u002F\u002Farxiv.org\u002Fhtml\u002F2301.05206v3\u002Fpics\u002Fcover_v4.jpg","\u002Ffigure-files\u002Flin2023immesh\u002Ffig-1.webp",1400,421,"\u002Ffigure-files\u002Flin2023immesh\u002Ffig-1.thumb.webp",480,144,"resized to at most 1400 px wide and converted to WebP",{"refId":5,"refLabel":6,"fig":159,"whatZh":160,"license":148,"licenseUrl":149,"sourceUrl":161,"src":162,"width":152,"height":163,"thumb":164,"thumbWidth":155,"thumbHeight":165,"modified":157},"Fig. 2","系統架構：接收、定位、地圖結構、建網格與廣播模組","https:\u002F\u002Farxiv.org\u002Fhtml\u002F2301.05206v3\u002Fpics\u002Foverview_v15.jpg","\u002Ffigure-files\u002Flin2023immesh\u002Ffig-2.webp",781,"\u002Ffigure-files\u002Flin2023immesh\u002Ffig-2.thumb.webp",268,{"refId":5,"refLabel":6,"fig":167,"whatZh":168,"license":148,"licenseUrl":149,"sourceUrl":169,"src":170,"width":152,"height":171,"thumb":172,"thumbWidth":155,"thumbHeight":173,"modified":157},"Fig. 6","手持資料收集裝置（迷你電腦、Livox Avia 光達、預覽相機）與實驗影片畫面","https:\u002F\u002Farxiv.org\u002Fhtml\u002F2301.05206v3\u002Fpics\u002Fhardware_v3.jpg","\u002Ffigure-files\u002Flin2023immesh\u002Ffig-6.webp",482,"\u002Ffigure-files\u002Flin2023immesh\u002Ffig-6.thumb.webp",165,{"refId":5,"refLabel":6,"fig":175,"whatZh":176,"license":148,"licenseUrl":149,"sourceUrl":177,"src":178,"width":152,"height":179,"thumb":180,"thumbWidth":155,"thumbHeight":181,"modified":157},"Fig. 11","DJI M300 無人機載具與山區場景的網格、高程著色及無損紋理重建成果","https:\u002F\u002Farxiv.org\u002Fhtml\u002F2301.05206v3\u002Fpics\u002Fair_inspect_v6.jpg","\u002Ffigure-files\u002Flin2023immesh\u002Ffig-11.webp",1241,"\u002Ffigure-files\u002Flin2023immesh\u002Ffig-11.thumb.webp",425,{"totalRows":183,"groupCount":184,"groups":185,"others":1023},100,6,[186,458,603,806],{"slug":187,"group":188,"sourceId":189,"sourceLabel":190,"table":107,"selfRows":191,"metrics":192,"seqs":219,"entrants":228,"cells":239,"outcomes":450,"locators":452,"hardware":453,"wordings":455,"notes":456},"wang2025planarmesh-table-i","wang2025planarmesh:Table I","wang2025planarmesh","Wang et al., 2025a",27,[193,197,200,203,205,209,212,215,217],{"label":194,"unit":195,"statistic":196,"alignment":47},"Per-Scan Time (s)","s","not_reported",{"label":198,"unit":199,"statistic":196,"alignment":47},"File size (MB), PLY binary","MB",{"label":201,"unit":202,"statistic":196,"alignment":47},"Num of Faces","count",{"label":204,"unit":202,"statistic":196,"alignment":47},"Num of Vertices",{"label":206,"unit":207,"statistic":208,"alignment":47},"Mean distance to TLS ground truth (m)","m","mean",{"label":210,"unit":207,"statistic":211,"alignment":47},"Std of distance to TLS ground truth (m)","std",{"label":213,"unit":214,"statistic":196,"alignment":47},"Precision at 0.1 m","fraction",{"label":216,"unit":214,"statistic":196,"alignment":47},"Recall at 0.1 m",{"label":218,"unit":214,"statistic":196,"alignment":47},"F-Score at 0.1 m",[220,224,226],{"dataset":221,"sequence":222,"environment":223},"Oxford Spires","Christ Church 03 (about 307 m)","existing buildings, indoor and outdoor (walking survey)",{"dataset":221,"sequence":225,"environment":223},"Keble College 03 (about 108 m)",{"dataset":221,"sequence":227,"environment":223},"Observatory 01 (about 324 m)",[229,233,234,236],{"name":230,"methodId":231,"linkable":232,"proposed":79,"self":79},"VDBFusion","vizzo2022vdbfusion",true,{"name":7,"methodId":5,"linkable":232,"proposed":79,"self":232},{"name":235,"methodId":189,"linkable":232,"proposed":232,"self":79},"PlanarMesh (Ours)",{"name":237,"methodId":238,"linkable":232,"proposed":79,"self":79},"OctoMap","hornung2013octomap",[240,244,247,250,253,256,259,261,264,267,269,271,273,275,277,279,281,283,285,287,289,291,293,295,297,299,301,303,305,307,308,309,311,313,314,316,318,320,322,324,326,328,330,332,334,335,337,339,341,343,345,347,349,350,352,354,356,358,360,362,364,366,368,370,372,374,375,376,377,379,380,381,383,385,387,389,391,393,395,397,398,399,401,403,405,407,409,411,413,415,417,419,421,423,425,427,429,431,433,435,437,439,440,441,443,445,447,449],[241,241,241,242,243,241,241,243,241],0,0.871,-1,[241,245,241,246,243,241,243,243,241],1,53.6,[241,248,241,249,243,241,243,243,241],2,1992391,[241,251,241,252,243,241,243,243,241],3,1152788,[241,254,241,255,243,241,243,243,241],4,0.044,[241,257,241,258,243,241,243,243,241],5,0.077,[241,184,241,260,243,241,243,243,241],0.918,[241,262,241,263,243,241,243,243,241],7,0.97,[241,265,241,266,243,241,243,243,241],8,0.943,[245,241,241,268,243,241,241,243,241],0.724,[245,245,241,270,243,241,243,243,241],370.9,[245,248,241,272,243,241,243,243,241],21180823,[245,251,241,274,243,241,243,243,241],7959789,[245,254,241,276,243,241,243,243,241],0.09,[245,257,241,278,243,241,243,243,241],0.186,[245,184,241,280,243,241,243,243,241],0.82,[245,262,241,282,243,241,243,243,241],0.99,[245,265,241,284,243,241,243,243,241],0.897,[248,241,241,286,243,241,241,243,241],0.392,[248,245,241,288,243,241,243,243,241],10.1,[248,248,241,290,243,241,243,243,241],398712,[248,251,241,292,243,241,243,243,241],411907,[248,254,241,294,243,241,243,243,241],0.037,[248,257,241,296,243,241,243,243,241],0.081,[248,184,241,298,243,241,243,243,241],0.951,[248,262,241,300,243,241,243,243,241],0.964,[248,265,241,302,243,241,243,243,241],0.957,[251,241,241,304,243,241,241,243,241],0.432,[251,245,241,306,243,241,243,243,241],3.4,[251,248,241,88,241,241,243,243,241],[251,251,241,88,241,241,243,243,241],[251,254,241,310,243,241,243,243,241],0.04,[251,257,241,312,243,241,243,243,241],0.083,[251,184,241,266,243,241,243,243,241],[251,262,241,315,243,241,243,243,241],0.991,[251,265,241,317,243,241,243,243,241],0.966,[241,241,245,319,243,241,241,243,241],0.968,[241,245,245,321,243,241,243,243,241],51.3,[241,248,245,323,243,241,243,243,241],1821087,[241,251,245,325,243,241,243,243,241],1150250,[241,254,245,327,243,241,243,243,241],0.033,[241,257,245,329,243,241,243,243,241],0.113,[241,184,245,331,243,241,243,243,241],0.962,[241,262,245,333,243,241,243,243,241],0.94,[241,265,245,298,243,241,243,243,241],[245,241,245,336,243,241,241,243,241],0.355,[245,245,245,338,243,241,243,243,241],163.8,[245,248,245,340,243,241,243,243,241],9057110,[245,251,245,342,243,241,243,243,241],3838040,[245,254,245,344,243,241,243,243,241],0.035,[245,257,245,346,243,241,243,243,241],0.064,[245,184,245,348,243,241,243,243,241],0.955,[245,262,245,260,243,241,243,243,241],[245,265,245,351,243,241,243,243,241],0.936,[248,241,245,353,243,241,241,243,241],0.416,[248,245,245,355,243,241,243,243,241],7.3,[248,248,245,357,243,241,243,243,241],287020,[248,251,245,359,243,241,243,243,241],296254,[248,254,245,361,243,241,243,243,241],0.031,[248,257,245,363,243,241,243,243,241],0.134,[248,184,245,365,243,241,243,243,241],0.979,[248,262,245,367,243,241,243,243,241],0.894,[248,265,245,369,243,241,243,243,241],0.935,[251,241,245,371,243,241,241,243,241],0.328,[251,245,245,373,243,241,243,243,241],24.3,[251,248,245,88,241,241,243,243,241],[251,251,245,88,241,241,243,243,241],[251,254,245,310,243,241,243,243,241],[251,257,245,378,243,241,243,243,241],0.159,[251,184,245,300,243,241,243,243,241],[251,262,245,317,243,241,243,243,241],[251,265,245,382,243,241,243,243,241],0.965,[241,241,248,384,243,241,241,243,241],2.406,[241,245,248,386,243,241,243,243,241],148.5,[241,248,248,388,243,241,243,243,241],5246193,[241,251,248,390,243,241,243,243,241],3346024,[241,254,248,392,243,241,243,243,241],0.047,[241,257,248,394,243,241,243,243,241],0.104,[241,184,248,396,243,241,243,243,241],0.899,[241,262,248,396,243,241,243,243,241],[241,265,248,396,243,241,243,243,241],[245,241,248,400,243,241,241,243,241],0.448,[245,245,248,402,243,241,243,243,241],424.7,[245,248,248,404,243,241,243,243,241],23448665,[245,251,248,406,243,241,243,243,241],9986250,[245,254,248,408,243,241,243,243,241],0.056,[245,257,248,410,243,241,243,243,241],0.089,[245,184,248,412,243,241,243,243,241],0.878,[245,262,248,414,243,241,243,243,241],0.832,[245,265,248,416,243,241,243,243,241],0.854,[248,241,248,418,243,241,241,243,241],0.213,[248,245,248,420,243,241,243,243,241],15.3,[248,248,248,422,243,241,243,243,241],546415,[248,251,248,424,243,241,243,243,241],682725,[248,254,248,426,243,241,243,243,241],0.042,[248,257,248,428,243,241,243,243,241],0.114,[248,184,248,430,243,241,243,243,241],0.929,[248,262,248,432,243,241,243,243,241],0.847,[248,265,248,434,243,241,243,243,241],0.886,[251,241,248,436,243,241,241,243,241],0.659,[251,245,248,438,243,241,243,243,241],67.8,[251,248,248,88,241,241,243,243,241],[251,251,248,88,241,241,243,243,241],[251,254,248,442,243,241,243,243,241],0.055,[251,257,248,444,243,241,243,243,241],0.15,[251,184,248,446,243,241,243,243,241],0.896,[251,262,248,448,243,241,243,243,241],0.941,[251,265,248,260,243,241,243,243,241],[451],"not_applicable (N\u002FA: occupancy map has no faces or vertices)",[107],[454],"28-core Intel i7 CPU, no GPU; PlanarMesh uses all cores, baselines one core each (Sec. IV-A)",[],[457],"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":459,"group":460,"sourceId":5,"sourceLabel":6,"table":461,"selfRows":462,"metrics":463,"seqs":488,"entrants":499,"cells":504,"outcomes":596,"locators":597,"hardware":598,"wordings":600,"notes":601},"lin2023immesh-table-v","lin2023immesh:Table V","Table V",24,[464,467,470,472,474,477,479,481,484,486],{"label":465,"unit":466,"statistic":208,"alignment":47},"Max-Min angle (deg), lower is better","deg",{"label":468,"unit":469,"statistic":208,"alignment":47},"C2SE (circumradius to shortest edge ratio), lower is better","ratio",{"label":471,"unit":207,"statistic":208,"alignment":47},"Completeness (m)",{"label":473,"unit":207,"statistic":208,"alignment":47},"Accuracy (m)",{"label":475,"unit":476,"statistic":196,"alignment":47},"Recall (%) at 5 cm","fraction (header says %)",{"label":478,"unit":476,"statistic":196,"alignment":47},"Precision (%) at 5 cm",{"label":480,"unit":214,"statistic":196,"alignment":47},"F-score at 5 cm",{"label":482,"unit":483,"statistic":196,"alignment":47},"Cost time (hour:min:sec) 00:05:39","s (converted from h:min:s)",{"label":485,"unit":483,"statistic":196,"alignment":47},"Cost time (hour:min:sec) 00:03:01",{"label":487,"unit":483,"statistic":196,"alignment":47},"Cost time (hour:min:sec) 00:03:07",[489,493,496],{"dataset":490,"sequence":491,"environment":492},"Complex Urban Dataset","Urban01","urban driving, 11.72 km, 13846 frames",{"dataset":490,"sequence":494,"environment":495},"Urban02","urban driving, 4.20 km, 8961 frames",{"dataset":490,"sequence":497,"environment":498},"Urban03","urban driving, 3.06 km, 9091 frames",[500,502],{"name":501,"methodId":88,"linkable":79,"proposed":79,"self":79},"Poi",{"name":503,"methodId":5,"linkable":232,"proposed":232,"self":232},"ImMesh (ours)",[505,507,509,511,513,515,517,519,521,523,525,527,529,531,533,535,537,539,541,543,545,547,549,551,553,555,557,559,561,563,565,567,569,571,573,575,577,579,582,584,586,588,590,592,594],[241,241,241,506,243,241,243,243,241],60.1014,[241,245,241,508,243,241,243,243,241],0.976,[241,248,241,510,243,241,243,243,241],0.0632,[241,251,241,512,243,241,243,243,241],0.0724,[241,254,241,514,243,241,243,243,241],0.8554,[241,257,241,516,243,241,243,243,241],0.7563,[241,184,241,518,243,241,243,243,241],0.8028,[245,262,241,520,243,241,241,243,241],339,[245,241,241,522,243,241,243,243,241],56.2941,[245,245,241,524,243,241,243,243,241],0.863,[245,248,241,526,243,241,243,243,241],0.0404,[245,251,241,528,243,241,243,243,241],0.0568,[245,254,241,530,243,241,243,243,241],0.9477,[245,257,241,532,243,241,243,243,241],0.826,[245,184,241,534,243,241,243,243,241],0.8827,[241,241,245,536,243,241,243,243,241],59.9695,[241,245,245,538,243,241,243,243,241],0.9739,[241,248,245,540,243,241,243,243,241],0.0792,[241,251,245,542,243,241,243,243,241],0.0822,[241,254,245,544,243,241,243,243,241],0.8818,[241,257,245,546,243,241,243,243,241],0.7261,[241,184,245,548,243,241,243,243,241],0.7964,[245,265,245,550,243,241,241,243,241],181,[245,241,245,552,243,241,243,243,241],57.3564,[245,245,245,554,243,241,243,243,241],0.8605,[245,248,245,556,243,241,243,243,241],0.0392,[245,251,245,558,243,241,243,243,241],0.0556,[245,254,245,560,243,241,243,243,241],0.9623,[245,257,245,562,243,241,243,243,241],0.8398,[245,184,245,564,243,241,243,243,241],0.8968,[241,241,248,566,243,241,243,243,241],60.1614,[241,245,248,568,243,241,243,243,241],0.977,[241,248,248,570,243,241,243,243,241],0.007,[241,251,248,572,243,241,243,243,241],0.0059,[241,254,248,574,243,241,243,243,241],0.8871,[241,257,248,576,243,241,243,243,241],0.7754,[241,184,248,578,243,241,243,243,241],0.8275,[245,580,248,581,243,241,241,243,241],9,187,[245,241,248,583,243,241,243,243,241],57.4131,[245,245,248,585,243,241,243,243,241],0.8628,[245,248,248,587,243,241,243,243,241],0.0398,[245,251,248,589,243,241,243,243,241],0.0564,[245,254,248,591,243,241,243,243,241],0.9597,[245,257,248,593,243,241,243,243,241],0.8359,[245,184,248,595,243,241,243,243,241],0.8935,[],[461],[599],"Desktop: Intel i7-9700K CPU, 64 GB RAM, Nvidia 2080 Ti 12 GB (GPU used only by TSDF)",[],[602],"Complex Urban Dataset; ImMesh fed frame by frame with ground-truth poses (pose estimation disabled); Poisson (official implementation, octree level 12, facets with an edge over 15 cm removed) fed the accumulated cloud voxel-filtered at 1 cm; meshes sampled at 0.01 m, precision and recall at 5 cm; cost time is total processing time converted from h:min:s; Del crashed or gave no result after 3 days and TSDF exceeded 12 GB GPU memory, so both are absent",{"slug":604,"group":605,"sourceId":5,"sourceLabel":6,"table":606,"selfRows":462,"metrics":607,"seqs":624,"entrants":639,"cells":646,"outcomes":800,"locators":801,"hardware":802,"wordings":803,"notes":804},"lin2023immesh-table-vi","lin2023immesh:Table VI","Table VI",[608,609,610,611,614,616,618,620,622],{"label":471,"unit":207,"statistic":208,"alignment":47},{"label":473,"unit":207,"statistic":208,"alignment":47},{"label":480,"unit":214,"statistic":196,"alignment":47},{"label":612,"unit":613,"statistic":196,"alignment":47},"Cost time (min:sec) 00:31","s (converted from min:sec)",{"label":615,"unit":613,"statistic":196,"alignment":47},"Cost time (min:sec) 00:23",{"label":617,"unit":613,"statistic":196,"alignment":47},"Cost time (min:sec) 00:19",{"label":619,"unit":613,"statistic":196,"alignment":47},"Cost time (min:sec) 00:33",{"label":621,"unit":613,"statistic":196,"alignment":47},"Cost time (min:sec) 00:25",{"label":623,"unit":613,"statistic":196,"alignment":47},"Cost time (min:sec) 00:21",[625,629,631,633,635,637],{"dataset":626,"sequence":627,"environment":628},"AirSim synthetic","Urban city, depth 640 x 480","simulation (AirSim)",{"dataset":626,"sequence":630,"environment":628},"Urban city, depth 320 x 240",{"dataset":626,"sequence":632,"environment":628},"Urban city, depth 160 x 120",{"dataset":626,"sequence":634,"environment":628},"Cluttered field, depth 640 x 480",{"dataset":626,"sequence":636,"environment":628},"Cluttered field, depth 320 x 240",{"dataset":626,"sequence":638,"environment":628},"Cluttered field, depth 160 x 120",[640,642,643,644],{"name":641,"methodId":88,"linkable":79,"proposed":79,"self":79},"Del",{"name":503,"methodId":5,"linkable":232,"proposed":232,"self":232},{"name":501,"methodId":88,"linkable":79,"proposed":79,"self":79},{"name":645,"methodId":88,"linkable":79,"proposed":79,"self":79},"TSDF",[647,649,651,653,655,657,659,661,663,665,667,669,671,673,675,677,678,680,681,682,684,686,688,690,692,694,696,698,700,702,704,706,708,710,712,714,716,718,720,722,724,726,728,730,732,734,736,738,740,742,744,746,748,750,752,754,756,758,760,762,764,766,768,770,772,774,776,778,780,782,784,786,788,790,792,794,796,798],[241,241,241,648,243,241,243,243,241],0.0883,[241,245,241,650,243,241,243,243,241],0.0341,[241,248,241,652,243,241,243,243,241],0.7976,[245,251,241,654,243,241,241,243,241],31,[245,241,241,656,243,241,243,243,241],0.1002,[245,245,241,658,243,241,243,243,241],0.0265,[245,248,241,660,243,241,243,243,241],0.7859,[248,241,241,662,243,241,243,243,241],0.1094,[248,245,241,664,243,241,243,243,241],0.2244,[248,248,241,666,243,241,243,243,241],0.7576,[251,241,241,668,243,241,243,243,241],0.1506,[251,245,241,670,243,241,243,243,241],0.0361,[251,248,241,672,243,241,243,243,241],0.6991,[241,241,245,674,243,241,243,243,241],0.0928,[241,245,245,676,243,241,243,243,241],0.0515,[241,248,245,666,243,241,243,243,241],[245,254,245,679,243,241,241,243,241],23,[245,241,245,656,243,241,243,243,241],[245,245,245,658,243,241,243,243,241],[245,248,245,683,243,241,243,243,241],0.7546,[248,241,245,685,243,241,243,243,241],0.1216,[248,245,245,687,243,241,243,243,241],0.0788,[248,248,245,689,243,241,243,243,241],0.6875,[251,241,245,691,243,241,243,243,241],0.1544,[251,245,245,693,243,241,243,243,241],0.0655,[251,248,245,695,243,241,243,243,241],0.5962,[241,241,248,697,243,241,243,243,241],0.1186,[241,245,248,699,243,241,243,243,241],0.0914,[241,248,248,701,243,241,243,243,241],0.6135,[245,257,248,703,243,241,241,243,241],19,[245,241,248,705,243,241,243,243,241],0.1341,[245,245,248,707,243,241,243,243,241],0.0834,[245,248,248,709,243,241,243,243,241],0.5696,[248,241,248,711,243,241,243,243,241],0.1849,[248,245,248,713,243,241,243,243,241],0.1453,[248,248,248,715,243,241,243,243,241],0.5381,[251,241,248,717,243,241,243,243,241],0.2802,[251,245,248,719,243,241,243,243,241],0.2508,[251,248,248,721,243,241,243,243,241],0.3947,[241,241,251,723,243,241,243,243,241],0.2767,[241,245,251,725,243,241,243,243,241],0.0496,[241,248,251,727,243,241,243,243,241],0.7255,[245,184,251,729,243,241,241,243,241],33,[245,241,251,731,243,241,243,243,241],0.2953,[245,245,251,733,243,241,243,243,241],0.0519,[245,248,251,735,243,241,243,243,241],0.7211,[248,241,251,737,243,241,243,243,241],0.3052,[248,245,251,739,243,241,243,243,241],0.396,[248,248,251,741,243,241,243,243,241],0.6995,[251,241,251,743,243,241,243,243,241],0.413,[251,245,251,745,243,241,243,243,241],0.427,[251,248,251,747,243,241,243,243,241],0.4886,[241,241,254,749,243,241,243,243,241],0.2919,[241,245,254,751,243,241,243,243,241],0.0882,[241,248,254,753,243,241,243,243,241],0.6181,[245,262,254,755,243,241,241,243,241],25,[245,241,254,757,243,241,243,243,241],0.3105,[245,245,254,759,243,241,243,243,241],0.0784,[245,248,254,761,243,241,243,243,241],0.6272,[248,241,254,763,243,241,243,243,241],0.362,[248,245,254,765,243,241,243,243,241],0.3395,[248,248,254,767,243,241,243,243,241],0.5567,[251,241,254,769,243,241,243,243,241],0.5268,[251,245,254,771,243,241,243,243,241],0.4784,[251,248,254,773,243,241,243,243,241],0.2319,[241,241,257,775,243,241,243,243,241],0.3438,[241,245,257,777,243,241,243,243,241],0.1781,[241,248,257,779,243,241,243,243,241],0.4663,[245,265,257,781,243,241,241,243,241],21,[245,241,257,783,243,241,243,243,241],0.3512,[245,245,257,785,243,241,243,243,241],0.1694,[245,248,257,787,243,241,243,243,241],0.4507,[248,241,257,789,243,241,243,243,241],0.3541,[248,245,257,791,243,241,243,243,241],0.4164,[248,248,257,793,243,241,243,243,241],0.3797,[251,241,257,795,243,241,243,243,241],0.5561,[251,245,257,797,243,241,243,243,241],0.3681,[251,248,257,799,243,241,243,243,241],0.2451,[],[606],[599],[],[805],"AirSim synthetic scenes (20 m x 10 m x 8 m) from depth images (FoV 120 x 80 deg) at three resolutions; ImMesh and TSDF (PCL, GPU, 0.2 m cells) given ground-truth poses; Del (OpenMVS Delaunay plus graph cut) and Poi given the accumulated cloud voxel-filtered at 1 cm; 5 cm threshold; cost time converted from min:sec",{"slug":807,"group":808,"sourceId":809,"sourceLabel":810,"table":811,"selfRows":812,"metrics":813,"seqs":818,"entrants":852,"cells":868,"outcomes":1014,"locators":1018,"hardware":1019,"wordings":1020,"notes":1021},"tao2025oxfordspires-table-3","tao2025oxfordspires:Table 3","tao2025oxfordspires","Tao et al., 2025","Table 3",14,[814],{"label":815,"unit":207,"statistic":816,"alignment":817},"RMS of ATE","RMSE","SE3",[819,822,824,826,828,831,833,836,838,840,843,845,847,849],{"dataset":221,"sequence":820,"environment":821},"Keble College 02 (290 m)","historic site, outdoor and indoor parts (Keble College, Oxford)",{"dataset":221,"sequence":823,"environment":821},"Keble College 03 (280 m)",{"dataset":221,"sequence":825,"environment":821},"Keble College 04 (780 m)",{"dataset":221,"sequence":827,"environment":821},"Keble College 05 (710 m)",{"dataset":221,"sequence":829,"environment":830},"Radcliffe Observatory Quarter 01 (400 m)","historic site, outdoor and indoor parts (Radcliffe Observatory Quarter, Oxford)",{"dataset":221,"sequence":832,"environment":830},"Radcliffe Observatory Quarter 02 (390 m)",{"dataset":221,"sequence":834,"environment":835},"Blenheim Palace 01 (490 m)","historic site, outdoor and indoor parts (Blenheim Palace, Oxford)",{"dataset":221,"sequence":837,"environment":835},"Blenheim Palace 02 (390 m)",{"dataset":221,"sequence":839,"environment":835},"Blenheim Palace 05 (390 m)",{"dataset":221,"sequence":841,"environment":842},"Christ Church College 01 (920 m)","historic site, outdoor and indoor parts (Christ Church College, Oxford)",{"dataset":221,"sequence":844,"environment":842},"Christ Church College 02 (640 m)",{"dataset":221,"sequence":846,"environment":842},"Christ Church College 03 (340 m)",{"dataset":221,"sequence":848,"environment":842},"Christ Church College 05 (820 m)",{"dataset":221,"sequence":850,"environment":851},"Bodleian Library 02 (690 m)","historic site, outdoor and indoor parts (Bodleian Library, Oxford)",[853,855,857,859,860,863,866],{"name":854,"methodId":88,"linkable":79,"proposed":79,"self":79},"VILENS-SLAM",{"name":856,"methodId":88,"linkable":79,"proposed":79,"self":79},"Fast-LIO-SLAM",{"name":858,"methodId":88,"linkable":79,"proposed":79,"self":79},"SC-LIO-SAM",{"name":7,"methodId":5,"linkable":232,"proposed":79,"self":232},{"name":861,"methodId":862,"linkable":232,"proposed":79,"self":79},"Fast-LIVO2","fastlivo2_2025",{"name":864,"methodId":865,"linkable":232,"proposed":79,"self":79},"HBA","hba2023",{"name":867,"methodId":88,"linkable":79,"proposed":79,"self":79},"COLMAP",[869,871,873,875,877,879,881,883,885,886,888,889,890,892,893,895,897,898,900,901,902,904,905,907,908,910,911,912,913,914,916,918,920,921,922,923,924,926,927,929,930,931,932,934,936,938,939,940,942,943,944,945,947,949,951,952,953,955,957,958,959,961,962,963,964,966,967,969,971,972,973,975,976,977,979,981,982,983,986,987,988,989,990,991,992,994,996,997,998,999,1000,1001,1004,1005,1007,1009,1011,1013],[241,241,241,870,243,241,243,243,241],0.06,[245,241,241,872,243,241,243,243,241],0.25,[248,241,241,874,243,241,243,243,241],1.26,[251,241,241,876,243,241,243,243,241],0.08,[254,241,241,878,243,241,243,243,241],0.95,[257,241,241,880,243,241,243,243,241],0.11,[184,241,241,882,243,241,243,243,241],0.05,[241,241,245,884,243,241,243,243,241],0.14,[245,241,245,880,243,241,243,243,241],[248,241,245,887,243,241,243,243,241],4.02,[251,241,245,884,243,241,243,243,241],[254,241,245,870,243,241,243,243,241],[257,241,245,891,243,241,243,243,241],0.12,[184,241,245,882,243,241,243,243,241],[241,241,248,894,243,241,243,243,241],0.16,[245,241,248,896,243,241,243,243,241],0.49,[248,241,248,88,241,241,243,243,241],[251,241,248,899,243,241,243,243,241],3.67,[254,241,248,276,243,241,243,243,241],[257,241,248,891,243,241,243,243,241],[184,241,248,903,243,241,243,243,241],0.07,[241,241,251,880,243,241,243,243,241],[245,241,251,906,243,241,243,243,241],0.29,[248,241,251,88,241,241,243,243,241],[251,241,251,909,243,241,243,243,241],0.13,[254,241,251,880,243,241,243,243,241],[257,241,251,909,243,241,243,243,241],[184,241,251,276,243,241,243,243,241],[241,241,254,870,243,241,243,243,241],[245,241,254,915,243,241,243,243,241],0.17,[248,241,254,917,243,241,243,243,241],0.23,[251,241,254,919,243,241,243,243,241],0.2,[254,241,254,310,243,241,243,243,241],[257,241,254,882,243,241,243,243,241],[184,241,254,903,243,241,243,243,241],[241,241,257,276,243,241,243,243,241],[245,241,257,925,243,241,243,243,241],0.24,[248,241,257,884,243,241,243,243,241],[251,241,257,928,243,241,243,243,241],0.27,[254,241,257,903,243,241,243,243,241],[257,241,257,876,243,241,243,243,241],[184,241,257,876,243,241,243,243,241],[241,241,184,933,243,241,243,243,241],0.47,[245,241,184,935,243,241,243,243,241],0.18,[248,241,184,937,243,241,243,243,241],6.74,[251,241,184,928,243,241,243,243,241],[254,241,184,884,243,241,243,243,241],[257,241,184,941,243,241,243,243,241],0.21,[184,241,184,876,243,241,243,243,241],[241,241,262,894,243,241,243,243,241],[245,241,262,891,243,241,243,243,241],[248,241,262,946,243,241,243,243,241],4.41,[251,241,262,948,243,241,243,243,241],0.36,[254,241,262,950,243,241,243,243,241],0.22,[257,241,262,876,243,241,243,243,241],[184,241,262,882,243,241,243,243,241],[241,241,265,954,243,241,243,243,241],1.05,[245,241,265,956,243,241,243,243,241],0.28,[248,241,265,88,241,241,243,243,241],[251,241,265,950,243,241,243,243,241],[254,241,265,960,243,241,243,243,241],0.26,[257,241,265,884,243,241,243,243,241],[184,241,265,960,243,241,243,243,241],[241,241,580,870,243,241,243,243,241],[245,241,580,965,243,241,243,243,241],0.72,[248,241,580,88,241,241,243,243,241],[251,241,580,968,243,241,243,243,241],0.19,[254,241,580,970,243,241,243,243,241],0.54,[257,241,580,903,243,241,243,243,241],[184,241,580,870,243,241,243,243,241],[241,241,974,915,243,241,243,243,241],10,[245,241,974,896,243,241,243,243,241],[248,241,974,88,241,241,243,243,241],[251,241,974,978,243,241,243,243,241],1.7,[254,241,974,980,243,241,243,243,241],0.63,[257,241,974,891,243,241,243,243,241],[184,241,974,444,243,241,243,243,241],[241,241,984,985,243,241,243,243,241],11,0.03,[245,241,984,917,243,241,243,243,241],[248,241,984,884,243,241,243,243,241],[251,241,984,894,243,241,243,243,241],[254,241,984,88,245,241,243,243,241],[257,241,984,882,243,241,243,243,241],[184,241,984,903,243,241,243,243,241],[241,241,993,915,243,241,243,243,241],12,[245,241,993,995,243,241,243,243,241],0.3,[248,241,993,88,241,241,243,243,241],[251,241,993,941,243,241,243,243,241],[254,241,993,444,243,241,243,243,241],[257,241,993,891,243,241,243,243,241],[184,241,993,88,248,241,243,243,241],[241,241,1002,1003,243,241,243,243,241],13,1.11,[245,241,1002,872,243,241,243,243,241],[248,241,1002,1006,243,241,243,243,241],1.71,[251,241,1002,1008,243,241,243,243,241],0.39,[254,241,1002,1010,243,241,243,243,241],0.46,[257,241,1002,1012,243,241,243,243,241],0.89,[184,241,1002,928,243,241,243,243,241],[1015,1016,1017],"failed","other: marked ✗ in Table 3 with no value; the caption explains ✗ only for SC-LIO-SAM failures and incomplete COLMAP results, so the reason for this Fast-LIVO2 entry is not stated","other: marked ✗ in Table 3; the caption states that COLMAP gives incomplete results on some sequences (Sec. 6.1.2: multiple disconnected sub-models under poor lighting)",[811],[],[],[1022],"ATE RMS (m) against LiDAR-to-TLS ground truth after SE(3) Umeyama alignment; online: VILENS-SLAM, Fast-LIO-SLAM, SC-LIO-SAM, ImMesh, Fast-LIVO2; offline: HBA (input VILENS-SLAM), COLMAP (images only). VILENS-SLAM = VILENS with pose-graph optimisation; Fast-LIO-SLAM and SC-LIO-SAM add Scan Context loop closures to Fast-LIO2 and LIO-SAM. 'x' in the table = failed or incomplete. Authors note methods could improve with further tuning.",[1024,1030],{"group":1025,"slug":1026,"sourceLabel":6,"table":1027,"selfRows":265,"datasets":1028},"lin2023immesh:Table IV","lin2023immesh-table-iv","Table IV",[105,110,115,1029],"R3LIVE",{"group":1031,"slug":1032,"sourceLabel":1033,"table":1034,"selfRows":251,"datasets":1035},"affan2026semanticmeshing:Table 1","affan2026semanticmeshing-table-1","Affan et al., 2026","Table 1",[221],1790510658316]