[{"data":1,"prerenderedAt":997},["ShallowReactive",2],{"method-vizzo2022vdbfusion":3},{"method":4,"reference":56,"equipment":78,"figures":131,"results":172},{"id":5,"label":6,"shortName":7,"title":8,"year":9,"era":10,"cluster":11,"scope":12,"keyIdeaZh":13,"keyIdeaEn":14,"fulltextStatus":15,"publicationStatus":16,"recommendation":17,"constructionRelevance":18,"validationEnvironment":19,"strengths":21,"limitations":27,"sensors":34,"platform":37,"estimator":41,"association":42,"timeModel":43,"deskew":44,"loopClosure":43,"globalOptimization":45,"mapRepresentation":46,"prior":47,"outputGeometry":48,"compute":49,"codeUrl":50,"codeLicense":51,"relatedVersions":52},"vizzo2022vdbfusion","Vizzo et al., 2022","VDBFusion","VDBFusion: Flexible and Efficient TSDF Integration of Range Sensor Data",2022,"recent","C12","map_representation_or_reconstruction","VDBFusion 以 OpenVDB 的階層稀疏體積結構儲存 TSDF，提供 C++ 與 Python 介面；權重函數可在執行時以 lambda 傳入，空間雕刻（space carving）可選擇開關，網格擷取可設最小權重門檻。作者說明截斷距離大有助抑制雜訊，但會造成薄面增厚；關閉空間雕刻較快，但會留下動態物體殘影。","A TSDF integration library on OpenVDB with runtime-selectable weighting, optional space carving and a minimum-weight mesh threshold, running on a single CPU core.","full_text_reviewed","peer_reviewed_published","supplementary","定量評估只用 KITTI 07（車載 LiDAR，準確度參考是同一序列的累積點雲）與 Cow and Lady（室內 RGB-D，參考點雲來自高解析度掃描儀），未涉及營建；截斷距離造成薄面增厚、空間雕刻會削去靜態物體邊緣等說明，與營建薄構件及臨時構件的量測直接相關（推論）。",[20],"public_benchmark",[22,23,24,25,26],"Integrates 64-beam LiDAR scans at 20 fps on a single CPU core without space carving (abstract; 19.57 fps on KITTI 07, Table 2).","Only three mapping parameters versus fourteen in Voxblox (Sec. 4.1, 4.5).","Smallest RAM footprint among tested variants: 847.0 MB on KITTI 07 versus 1.12 GB for Octomap and 2.95 GB for raw points (Table 4).","Mean point-to-point error 0.023 m (std 0.022 m) on KITTI 07 with space carving, versus 0.033 m for Octomap and 0.497 m for Voxblox (Table 6).","Python API nearly as fast as C++ (18.93 versus 19.57 fps on KITTI 07, Table 3); ten students built a working pipeline in under 1 h, 40 min on average (Sec. 5.5).",[28,29,30,31,32,33],"Large truncation distances thicken thin surfaces; small ones are sensitive to sensor noise (Sec. 4.5).","Space carving removes dynamic objects but cuts the rate to 1.37 fps on KITTI 07 and 0.84 fps on Cow and Lady, below Octomap's 1.05 fps there (Sec. 4.5, Table 2).","Space carving also removed parts of the static scene at car boundaries (Fig. 12).","(inference) The KITTI 07 accuracy reference is built by aggregating the KITTI LiDAR scans of the same sequence without downsampling and removing dynamic objects with SemanticKITTI labels (Sec. 5.4), so accuracy is not measured against an independent instrument; the authors do not discuss this.","The authors' Voxblox results do not match the original publication; they suspect a numerical error in Voxblox's transformation library, which weakens that comparison (Sec. 5.4).","No probabilistic occupancy model; an exported mesh cannot be updated after it is stored (Sec. 4.3, 5.3).",[35,36],"3D LiDAR","RGB-D",[38,39,40],"vehicle","handheld (Newer College, qualitative only)","simulation (ICL-NUIM synthetic RGB-D, qualitative only)","not_applicable (poses supplied; points assumed in the global frame)","ray casting of points into a TSDF within the truncation distance","not_applicable","delegated to dataset-specific data loaders (system section)","none","TSDF stored as two OpenVDB sparse grids (signed distance and weight); VDB leaf blocks are typically 8 x 8 x 8 voxels in a fixed-depth tree; truncation distance 3 voxels in the experiments; optional space carving; no occupancy probabilities (Sec. 3, 4.2, 4.3, 5)","external poses","triangle mesh via marching cubes adapted from Open3D to VDB, with optional hole filling (Curless and Levoy) and a runtime min_weight threshold that also removes dynamic objects; TSDF and weight grids saved as VDB files with lossless compression (Sec. 4.6, 5.3)","single CPU core without multithreading (Intel Xeon W-2145, 8 cores at 3.70 GHz, 32 GB RAM, GCC 9.3.0); KITTI 07 at 10 cm voxels 19.57 fps without and 1.37 fps with space carving; Cow and Lady at 2 mm voxels 14.14 fps and 0.84 fps (Sec. 5, Table 2)","https:\u002F\u002Fgithub.com\u002FPRBonn\u002Fvdbfusion","MIT (repository LICENSE)",[53],{"relation":54,"title":55,"doi_or_url":50},"code_release","PRBonn\u002Fvdbfusion",{"id":5,"kind":57,"shortName":7,"title":8,"authors":58,"year":9,"venue":63,"venueType":64,"publisher":65,"volumeIssuePages":66,"doi":67,"arxivId":68,"url":69,"firstPublicDate":70,"publicationStatus":16,"metadataStatus":71,"fulltextStatus":15,"era":10,"classicReason":43,"codeUrl":50,"cluster":11,"topics":72,"mdpi":73,"verification":74,"label":6,"fulltextRoute":75,"versionRead":76,"addedByCensus":77},"software",[59,60,61,62],"Ignacio Vizzo","Tiziano Guadagnino","Jens Behley","Cyrill Stachniss","Sensors","journal","MDPI","22(3):1296","10.3390\u002Fs22031296",null,"https:\u002F\u002Fwww.ebi.ac.uk\u002Feuropepmc\u002Fwebservices\u002Frest\u002FPMC8838740\u002FfullTextXML","2022-02-08","metadata_verified",[11],true,"confirmed","publisher OA","Version of record (MDPI Sensors 22(3):1296, CC BY 4.0) as Europe PMC full-text XML PMC8838740, including every section, Tables 1-6 and captions of Figs. 1-20",false,[79,85,92,97,102,107,112,118,122,126],{"category":80,"model":81,"canonical":81,"role":82,"dataset":68,"specs":83,"locator":84},"compute","Intel Xeon W-2145","compute for runtime","8 cores at 3.70 GHz, 32 GB RAM, GNU\u002FLinux 64-bit, GCC 9.3.0; every method run without multithreading","Sec. 5",{"category":86,"model":87,"canonical":87,"role":88,"dataset":89,"specs":90,"locator":91},"lidar","64-beam rotating Velodyne LiDAR (exact model not named)","dataset sensor","KITTI Odometry","mounted on a car roof; experiments used ranges 2-70 m and 10 cm voxels","Sec. 5; Sec. 5.6.1",{"category":86,"model":93,"canonical":93,"role":88,"dataset":94,"specs":95,"locator":96},"64-beam Ouster sensor (model not named)","Newer College","hand-held device through New College, Oxford; only LiDAR data used; qualitative result","Sec. 5.6.2",{"category":86,"model":98,"canonical":98,"role":88,"dataset":99,"specs":100,"locator":101},"32-beam Velodyne LiDAR scanner (model not named)","nuScenes","car roof; Boston and Singapore; scene-0061; qualitative result","Sec. 5.6.3",{"category":86,"model":103,"canonical":103,"role":88,"dataset":104,"specs":105,"locator":106},"Velodyne HDL-64E","Apollo-SouthBay","car roof; Columbia Park sequence; qualitative result","Sec. 5.6.4",{"category":108,"model":109,"canonical":109,"role":110,"dataset":104,"specs":111,"locator":106},"other","integrated navigation system (model not named)","reference or ground truth","source of the ground-truth poses used for the Apollo-SouthBay example",{"category":113,"model":114,"canonical":114,"role":88,"dataset":115,"specs":116,"locator":117},"rgbd","Microsoft Kinect","TUM RGB-D","colour and depth at 30 Hz, 640 x 480; freiburg1_xyz sequence; qualitative result","Sec. 5.6.6",{"category":113,"model":119,"canonical":119,"role":88,"dataset":120,"specs":121,"locator":84},"RGB-D camera of the Cow and Lady dataset (model not named)","Cow and Lady","points within 0.1-5 m used, 2 mm voxels, truncation 3 voxels",{"category":108,"model":123,"canonical":123,"role":110,"dataset":120,"specs":124,"locator":125},"high-resolution scanner (type and model not named)","reference point cloud supplied with the Cow and Lady dataset, used for the accuracy evaluation","Sec. 5.4",{"category":113,"model":127,"canonical":127,"role":88,"dataset":128,"specs":129,"locator":130},"synthetic RGB-D depth maps (ICL-NUIM Living room, no simulated noise)","ICL-NUIM","depth maps converted to point clouds with ground-truth camera poses; qualitative result","Sec. 5.6.5",[132,145,154,163],{"refId":5,"refLabel":6,"fig":133,"whatZh":134,"license":135,"licenseUrl":136,"sourceUrl":137,"src":138,"width":139,"height":140,"thumb":141,"thumbWidth":142,"thumbHeight":143,"modified":144},"Figure 1","VDBFusion 在多個公開 LiDAR 與 RGB-D 資料集的重建結果（藍色為 LiDAR、紅色為 RGB-D）","CC BY 4.0","https:\u002F\u002Fcreativecommons.org\u002Flicenses\u002Fby\u002F4.0\u002F","https:\u002F\u002Fcdn.ncbi.nlm.nih.gov\u002Fpmc\u002Fblobs\u002F99b2\u002F8838740\u002F39c13a94b579\u002Fsensors-22-01296-g001.jpg","\u002Ffigure-files\u002Fvizzo2022vdbfusion\u002Ffigure-1.webp",795,357,"\u002Ffigure-files\u002Fvizzo2022vdbfusion\u002Ffigure-1.thumb.webp",480,216,"converted to WebP",{"refId":5,"refLabel":6,"fig":146,"whatZh":147,"license":135,"licenseUrl":136,"sourceUrl":148,"src":149,"width":150,"height":151,"thumb":152,"thumbWidth":142,"thumbHeight":153,"modified":144},"Figure 3","系統總覽：輸入點雲與位姿，整合為稀疏 TSDF 後輸出網格或 VDB 資料","https:\u002F\u002Fcdn.ncbi.nlm.nih.gov\u002Fpmc\u002Fblobs\u002F99b2\u002F8838740\u002F77a922d80710\u002Fsensors-22-01296-g003.jpg","\u002Ffigure-files\u002Fvizzo2022vdbfusion\u002Ffigure-3.webp",714,266,"\u002Ffigure-files\u002Fvizzo2022vdbfusion\u002Ffigure-3.thumb.webp",179,{"refId":5,"refLabel":6,"fig":155,"whatZh":156,"license":135,"licenseUrl":136,"sourceUrl":157,"src":158,"width":159,"height":160,"thumb":161,"thumbWidth":142,"thumbHeight":162,"modified":144},"Figure 6","有無空間雕刻時沿射線更新的體素範圍差異","https:\u002F\u002Fcdn.ncbi.nlm.nih.gov\u002Fpmc\u002Fblobs\u002F99b2\u002F8838740\u002F510fea28631e\u002Fsensors-22-01296-g006.jpg","\u002Ffigure-files\u002Fvizzo2022vdbfusion\u002Ffigure-6.webp",700,295,"\u002Ffigure-files\u002Fvizzo2022vdbfusion\u002Ffigure-6.thumb.webp",202,{"refId":5,"refLabel":6,"fig":164,"whatZh":165,"license":135,"licenseUrl":136,"sourceUrl":166,"src":167,"width":168,"height":169,"thumb":170,"thumbWidth":142,"thumbHeight":171,"modified":144},"Figure 12","KITTI 07 地圖逐點誤差：未雕刻時動態物體殘影，雕刻後動態物體移除但車緣也被削去","https:\u002F\u002Fcdn.ncbi.nlm.nih.gov\u002Fpmc\u002Fblobs\u002F99b2\u002F8838740\u002F28f2d4e8371b\u002Fsensors-22-01296-g012.jpg","\u002Ffigure-files\u002Fvizzo2022vdbfusion\u002Ffigure-12.webp",716,342,"\u002Ffigure-files\u002Fvizzo2022vdbfusion\u002Ffigure-12.thumb.webp",229,{"totalRows":173,"groupCount":174,"groups":175,"others":945},110,14,[176,449,678,823],{"slug":177,"group":178,"sourceId":179,"sourceLabel":180,"table":181,"selfRows":182,"metrics":183,"seqs":210,"entrants":219,"cells":229,"outcomes":441,"locators":443,"hardware":444,"wordings":446,"notes":447},"wang2025planarmesh-table-i","wang2025planarmesh:Table I","wang2025planarmesh","Wang et al., 2025a","Table I",27,[184,188,191,194,196,200,203,206,208],{"label":185,"unit":186,"statistic":187,"alignment":45},"Per-Scan Time (s)","s","not_reported",{"label":189,"unit":190,"statistic":187,"alignment":45},"File size (MB), PLY binary","MB",{"label":192,"unit":193,"statistic":187,"alignment":45},"Num of Faces","count",{"label":195,"unit":193,"statistic":187,"alignment":45},"Num of Vertices",{"label":197,"unit":198,"statistic":199,"alignment":45},"Mean distance to TLS ground truth (m)","m","mean",{"label":201,"unit":198,"statistic":202,"alignment":45},"Std of distance to TLS ground truth (m)","std",{"label":204,"unit":205,"statistic":187,"alignment":45},"Precision at 0.1 m","fraction",{"label":207,"unit":205,"statistic":187,"alignment":45},"Recall at 0.1 m",{"label":209,"unit":205,"statistic":187,"alignment":45},"F-Score at 0.1 m",[211,215,217],{"dataset":212,"sequence":213,"environment":214},"Oxford Spires","Christ Church 03 (about 307 m)","existing buildings, indoor and outdoor (walking survey)",{"dataset":212,"sequence":216,"environment":214},"Keble College 03 (about 108 m)",{"dataset":212,"sequence":218,"environment":214},"Observatory 01 (about 324 m)",[220,221,224,226],{"name":7,"methodId":5,"linkable":73,"proposed":77,"self":73},{"name":222,"methodId":223,"linkable":73,"proposed":77,"self":77},"ImMesh","lin2023immesh",{"name":225,"methodId":179,"linkable":73,"proposed":73,"self":77},"PlanarMesh (Ours)",{"name":227,"methodId":228,"linkable":73,"proposed":77,"self":77},"OctoMap","hornung2013octomap",[230,234,237,240,243,246,249,252,255,258,260,262,264,266,268,270,272,274,276,278,280,282,284,286,288,290,292,294,296,298,299,300,302,304,305,307,309,311,313,315,317,319,321,323,325,326,328,330,332,334,336,338,340,341,343,345,347,349,351,353,355,357,359,361,363,365,366,367,368,370,371,372,374,376,378,380,382,384,386,388,389,390,392,394,396,398,400,402,404,406,408,410,412,414,416,418,420,422,424,426,428,430,431,432,434,436,438,440],[231,231,231,232,233,231,231,233,231],0,0.871,-1,[231,235,231,236,233,231,233,233,231],1,53.6,[231,238,231,239,233,231,233,233,231],2,1992391,[231,241,231,242,233,231,233,233,231],3,1152788,[231,244,231,245,233,231,233,233,231],4,0.044,[231,247,231,248,233,231,233,233,231],5,0.077,[231,250,231,251,233,231,233,233,231],6,0.918,[231,253,231,254,233,231,233,233,231],7,0.97,[231,256,231,257,233,231,233,233,231],8,0.943,[235,231,231,259,233,231,231,233,231],0.724,[235,235,231,261,233,231,233,233,231],370.9,[235,238,231,263,233,231,233,233,231],21180823,[235,241,231,265,233,231,233,233,231],7959789,[235,244,231,267,233,231,233,233,231],0.09,[235,247,231,269,233,231,233,233,231],0.186,[235,250,231,271,233,231,233,233,231],0.82,[235,253,231,273,233,231,233,233,231],0.99,[235,256,231,275,233,231,233,233,231],0.897,[238,231,231,277,233,231,231,233,231],0.392,[238,235,231,279,233,231,233,233,231],10.1,[238,238,231,281,233,231,233,233,231],398712,[238,241,231,283,233,231,233,233,231],411907,[238,244,231,285,233,231,233,233,231],0.037,[238,247,231,287,233,231,233,233,231],0.081,[238,250,231,289,233,231,233,233,231],0.951,[238,253,231,291,233,231,233,233,231],0.964,[238,256,231,293,233,231,233,233,231],0.957,[241,231,231,295,233,231,231,233,231],0.432,[241,235,231,297,233,231,233,233,231],3.4,[241,238,231,68,231,231,233,233,231],[241,241,231,68,231,231,233,233,231],[241,244,231,301,233,231,233,233,231],0.04,[241,247,231,303,233,231,233,233,231],0.083,[241,250,231,257,233,231,233,233,231],[241,253,231,306,233,231,233,233,231],0.991,[241,256,231,308,233,231,233,233,231],0.966,[231,231,235,310,233,231,231,233,231],0.968,[231,235,235,312,233,231,233,233,231],51.3,[231,238,235,314,233,231,233,233,231],1821087,[231,241,235,316,233,231,233,233,231],1150250,[231,244,235,318,233,231,233,233,231],0.033,[231,247,235,320,233,231,233,233,231],0.113,[231,250,235,322,233,231,233,233,231],0.962,[231,253,235,324,233,231,233,233,231],0.94,[231,256,235,289,233,231,233,233,231],[235,231,235,327,233,231,231,233,231],0.355,[235,235,235,329,233,231,233,233,231],163.8,[235,238,235,331,233,231,233,233,231],9057110,[235,241,235,333,233,231,233,233,231],3838040,[235,244,235,335,233,231,233,233,231],0.035,[235,247,235,337,233,231,233,233,231],0.064,[235,250,235,339,233,231,233,233,231],0.955,[235,253,235,251,233,231,233,233,231],[235,256,235,342,233,231,233,233,231],0.936,[238,231,235,344,233,231,231,233,231],0.416,[238,235,235,346,233,231,233,233,231],7.3,[238,238,235,348,233,231,233,233,231],287020,[238,241,235,350,233,231,233,233,231],296254,[238,244,235,352,233,231,233,233,231],0.031,[238,247,235,354,233,231,233,233,231],0.134,[238,250,235,356,233,231,233,233,231],0.979,[238,253,235,358,233,231,233,233,231],0.894,[238,256,235,360,233,231,233,233,231],0.935,[241,231,235,362,233,231,231,233,231],0.328,[241,235,235,364,233,231,233,233,231],24.3,[241,238,235,68,231,231,233,233,231],[241,241,235,68,231,231,233,233,231],[241,244,235,301,233,231,233,233,231],[241,247,235,369,233,231,233,233,231],0.159,[241,250,235,291,233,231,233,233,231],[241,253,235,308,233,231,233,233,231],[241,256,235,373,233,231,233,233,231],0.965,[231,231,238,375,233,231,231,233,231],2.406,[231,235,238,377,233,231,233,233,231],148.5,[231,238,238,379,233,231,233,233,231],5246193,[231,241,238,381,233,231,233,233,231],3346024,[231,244,238,383,233,231,233,233,231],0.047,[231,247,238,385,233,231,233,233,231],0.104,[231,250,238,387,233,231,233,233,231],0.899,[231,253,238,387,233,231,233,233,231],[231,256,238,387,233,231,233,233,231],[235,231,238,391,233,231,231,233,231],0.448,[235,235,238,393,233,231,233,233,231],424.7,[235,238,238,395,233,231,233,233,231],23448665,[235,241,238,397,233,231,233,233,231],9986250,[235,244,238,399,233,231,233,233,231],0.056,[235,247,238,401,233,231,233,233,231],0.089,[235,250,238,403,233,231,233,233,231],0.878,[235,253,238,405,233,231,233,233,231],0.832,[235,256,238,407,233,231,233,233,231],0.854,[238,231,238,409,233,231,231,233,231],0.213,[238,235,238,411,233,231,233,233,231],15.3,[238,238,238,413,233,231,233,233,231],546415,[238,241,238,415,233,231,233,233,231],682725,[238,244,238,417,233,231,233,233,231],0.042,[238,247,238,419,233,231,233,233,231],0.114,[238,250,238,421,233,231,233,233,231],0.929,[238,253,238,423,233,231,233,233,231],0.847,[238,256,238,425,233,231,233,233,231],0.886,[241,231,238,427,233,231,231,233,231],0.659,[241,235,238,429,233,231,233,233,231],67.8,[241,238,238,68,231,231,233,233,231],[241,241,238,68,231,231,233,233,231],[241,244,238,433,233,231,233,233,231],0.055,[241,247,238,435,233,231,233,233,231],0.15,[241,250,238,437,233,231,233,233,231],0.896,[241,253,238,439,233,231,233,233,231],0.941,[241,256,238,251,233,231,233,233,231],[442],"not_applicable (N\u002FA: occupancy map has no faces or vertices)",[181],[445],"28-core Intel i7 CPU, no GPU; PlanarMesh uses all cores, baselines one core each (Sec. IV-A)",[],[448],"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":450,"group":451,"sourceId":452,"sourceLabel":453,"table":454,"selfRows":455,"metrics":456,"seqs":466,"entrants":476,"cells":490,"outcomes":672,"locators":673,"hardware":674,"wordings":675,"notes":676},"pings2025-table-ii","pings2025:Table II","pings2025","Pan et al., 2025","Table II",16,[457,459,461,463],{"label":458,"unit":198,"statistic":187,"alignment":187},"Accuracy error",{"label":460,"unit":198,"statistic":187,"alignment":187},"Completeness error",{"label":462,"unit":198,"statistic":187,"alignment":187},"Chamfer Distance",{"label":464,"unit":465,"statistic":187,"alignment":187},"F-score (0.1 m threshold)","fraction (0 to 1)",[467,470,472,474],{"dataset":212,"sequence":468,"environment":469},"Blenheim Palace 05","Oxford Spires sequences Blenheim Palace 05, Christ Church 02, Keble College 04 and Observatory Quarter 01, handheld LiDAR-camera rig; scene type not described in the paper",{"dataset":212,"sequence":471,"environment":469},"Christ Church 02",{"dataset":212,"sequence":473,"environment":469},"Keble College 04",{"dataset":212,"sequence":475,"environment":469},"Observatory Quarter 01",[477,479,481,483,485,488],{"name":478,"methodId":68,"linkable":77,"proposed":77,"self":77},"OpenMVS [5] (offline)",{"name":480,"methodId":68,"linkable":77,"proposed":77,"self":77},"Nerfacto [62] (offline)",{"name":482,"methodId":68,"linkable":77,"proposed":77,"self":77},"GSS [11]",{"name":484,"methodId":5,"linkable":73,"proposed":77,"self":73},"VDB-Fusion [67]",{"name":486,"methodId":487,"linkable":73,"proposed":77,"self":77},"PIN-SLAM [51]","pinslam2024",{"name":489,"methodId":452,"linkable":73,"proposed":73,"self":77},"PINGS (Ours)",[491,493,495,497,499,501,503,505,507,509,511,513,515,517,519,521,523,525,527,529,531,533,535,537,539,541,543,545,547,549,551,553,555,557,559,561,563,564,566,568,570,572,574,576,578,580,582,583,585,586,588,590,592,594,595,597,599,600,602,604,606,608,610,611,613,615,617,618,620,622,624,626,628,630,631,633,635,637,639,641,643,645,647,649,651,652,654,656,658,660,662,664,666,667,669,670],[231,231,231,492,233,231,233,233,231],0.126,[231,235,231,494,233,231,233,233,231],1.045,[231,238,231,496,233,231,233,233,231],0.586,[231,241,231,498,233,231,233,233,231],0.458,[235,231,231,500,233,231,233,233,231],0.302,[235,235,231,502,233,231,233,233,231],0.676,[235,238,231,504,233,231,233,233,231],0.489,[235,241,231,506,233,231,233,233,231],0.309,[238,231,231,508,233,231,233,233,231],0.204,[238,235,231,510,233,231,233,233,231],0.254,[238,238,231,512,233,231,233,233,231],0.229,[238,241,231,514,233,231,233,233,231],0.266,[241,231,231,516,233,231,233,233,231],0.098,[241,235,231,518,233,231,233,233,231],0.123,[241,238,231,520,233,231,233,233,231],0.111,[241,241,231,522,233,231,233,233,231],0.692,[244,231,231,524,233,231,233,233,231],0.078,[244,235,231,526,233,231,233,233,231],0.136,[244,238,231,528,233,231,233,233,231],0.107,[244,241,231,530,233,231,233,233,231],0.739,[247,231,231,532,233,231,233,233,231],0.072,[247,235,231,534,233,231,233,233,231],0.133,[247,238,231,536,233,231,233,233,231],0.102,[247,241,231,538,233,231,233,233,231],0.758,[231,231,235,540,233,231,233,233,231],0.046,[231,235,235,542,233,231,233,233,231],5.381,[231,238,235,544,233,231,233,233,231],2.714,[231,241,235,546,233,231,233,233,231],0.41,[235,231,235,548,233,231,233,233,231],0.219,[235,235,235,550,233,231,233,233,231],4.435,[235,238,235,552,233,231,233,233,231],2.327,[235,241,235,554,233,231,233,233,231],0.343,[238,231,235,556,233,231,233,233,231],0.174,[238,235,235,558,233,231,233,233,231],0.292,[238,238,235,560,233,231,233,233,231],0.233,[238,241,235,562,233,231,233,233,231],0.346,[241,231,235,516,233,231,233,233,231],[241,235,235,565,233,231,233,233,231],0.243,[241,238,235,567,233,231,233,233,231],0.171,[241,241,235,569,233,231,233,233,231],0.582,[244,231,235,571,233,231,233,233,231],0.069,[244,235,235,573,233,231,233,233,231],0.252,[244,238,235,575,233,231,233,233,231],0.16,[244,241,235,577,233,231,233,233,231],0.617,[247,231,235,579,233,231,233,233,231],0.067,[247,235,235,581,233,231,233,233,231],0.251,[247,238,235,369,233,231,233,233,231],[247,241,235,584,233,231,233,233,231],0.622,[231,231,238,579,233,231,233,233,231],[231,235,238,587,233,231,233,233,231],0.342,[231,238,238,589,233,231,233,233,231],0.205,[231,241,238,591,233,231,233,233,231],0.806,[235,231,238,593,233,231,233,233,231],0.137,[235,235,238,435,233,231,233,233,231],[235,238,238,596,233,231,233,233,231],0.144,[235,241,238,598,233,231,233,233,231],0.68,[238,231,238,567,233,231,233,233,231],[238,235,238,601,233,231,233,233,231],0.162,[238,238,238,603,233,231,233,233,231],0.167,[238,241,238,605,233,231,233,233,231],0.466,[241,231,238,607,233,231,233,233,231],0.103,[241,235,238,609,233,231,233,233,231],0.101,[241,238,238,536,233,231,233,233,231],[241,241,238,612,233,231,233,233,231],0.719,[244,231,238,614,233,231,233,233,231],0.096,[244,235,238,616,233,231,233,233,231],0.108,[244,238,238,536,233,231,233,233,231],[244,241,238,619,233,231,233,233,231],0.744,[247,231,238,621,233,231,233,233,231],0.093,[247,235,238,623,233,231,233,233,231],0.106,[247,238,238,625,233,231,233,233,231],0.099,[247,241,238,627,233,231,233,233,231],0.749,[231,231,241,629,233,231,233,233,231],0.048,[231,235,241,584,233,231,233,233,231],[231,238,241,632,233,231,233,233,231],0.335,[231,241,241,634,233,231,233,233,231],0.734,[235,231,241,636,233,231,233,233,231],0.197,[235,235,241,638,233,231,233,233,231],0.398,[235,238,241,640,233,231,233,233,231],0.298,[235,241,241,642,233,231,233,233,231],0.592,[238,231,241,644,233,231,233,233,231],0.179,[238,235,241,646,233,231,233,233,231],0.184,[238,238,241,648,233,231,233,233,231],0.181,[238,241,241,650,233,231,233,233,231],0.407,[241,231,241,518,233,231,233,233,231],[241,235,241,653,233,231,233,233,231],0.109,[241,238,241,655,233,231,233,233,231],0.116,[241,241,241,657,233,231,233,233,231],0.645,[244,231,241,659,233,231,233,233,231],0.105,[244,235,241,661,233,231,233,233,231],0.129,[244,238,241,663,233,231,233,233,231],0.117,[244,241,241,665,233,231,233,233,231],0.665,[247,231,241,536,233,231,233,233,231],[247,235,241,668,233,231,233,233,231],0.124,[247,238,241,320,233,231,233,233,231],[247,241,241,671,233,231,233,233,231],0.681,[],[454],[],[],[677],"Oxford Spires surface reconstruction against the millimetre-accurate Leica RTC360 TLS reference map; localization disabled and ground-truth poses used for all methods; OpenMVS and Nerfacto results taken from the benchmark (offline batch); meshes at 0.1 m resolution; F-score threshold 0.1 m; precision and recall columns not extracted",{"slug":679,"group":680,"sourceId":681,"sourceLabel":682,"table":683,"selfRows":684,"metrics":685,"seqs":700,"entrants":706,"cells":720,"outcomes":816,"locators":817,"hardware":819,"wordings":820,"notes":821},"zhu2025meshloam-table-v","zhu2025meshloam:Table V","zhu2025meshloam","Zhu et al., 2025","Table V",10,[686,689,691,693,696,698],{"label":687,"unit":688,"statistic":187,"alignment":45},"Comp. (cm), completion","cm",{"label":690,"unit":688,"statistic":187,"alignment":45},"Acc. (cm), accuracy",{"label":692,"unit":688,"statistic":187,"alignment":45},"C-L1 (cm), Chamfer-L1 distance",{"label":694,"unit":695,"statistic":187,"alignment":45},"Comp.Ratio (%)","%",{"label":697,"unit":695,"statistic":187,"alignment":45},"F-score (10cm) (%)",{"label":699,"unit":695,"statistic":187,"alignment":45},"F-score (20cm) (%)",[701,704],{"dataset":702,"sequence":702,"environment":703},"Mai City","simulated urban street",{"dataset":94,"sequence":187,"environment":705},"outdoor college (handheld)",[707,709,712,715,718],{"name":708,"methodId":5,"linkable":73,"proposed":77,"self":73},"VDB Fusion [28]",{"name":710,"methodId":711,"linkable":73,"proposed":77,"self":77},"Puma [13]","vizzo2021puma",{"name":713,"methodId":714,"linkable":73,"proposed":77,"self":77},"SHINE-Mapping [30]","shinemapping2023",{"name":716,"methodId":717,"linkable":73,"proposed":77,"self":77},"SLAMesh [14]","ruan2023slamesh",{"name":719,"methodId":681,"linkable":73,"proposed":73,"self":77},"Ours",[721,723,725,727,729,731,733,734,736,738,740,742,744,746,748,750,752,754,756,758,760,762,764,766,768,770,771,773,775,777,779,781,783,785,787,789,791,793,795,797,799,801,802,804,806,808,810,811,813,815],[231,231,231,722,233,231,233,233,231],6.9,[231,235,231,724,233,231,233,233,231],1.3,[231,238,231,726,233,231,233,233,231],4.5,[231,241,231,728,233,231,233,233,231],90.2,[231,244,231,730,233,231,233,233,231],94.1,[231,231,235,732,233,231,233,233,231],12,[231,235,235,722,233,231,233,233,231],[231,238,235,735,233,231,233,233,231],9.4,[231,241,235,737,233,231,233,233,231],91.3,[231,247,235,739,233,231,233,233,231],92.6,[235,231,231,741,233,231,233,233,231],32,[235,235,231,743,233,231,233,233,231],1.2,[235,238,231,745,233,231,233,233,231],16.9,[235,241,231,747,233,231,233,233,231],78.8,[235,244,231,749,233,231,233,233,231],87.3,[235,231,235,751,233,231,233,233,231],15.4,[235,235,235,753,233,231,233,233,231],7.7,[235,238,235,755,233,231,233,233,231],11.5,[235,241,235,757,233,231,233,233,231],89.9,[235,247,235,759,233,231,233,233,231],91.9,[238,231,231,761,233,231,233,233,231],3.2,[238,235,231,763,233,231,233,233,231],1.1,[238,238,231,765,233,231,233,233,231],2.9,[238,241,231,767,233,231,233,233,231],95.2,[238,244,231,769,233,231,233,233,231],95.9,[238,231,235,684,233,231,233,233,231],[238,235,235,772,233,231,233,233,231],6.7,[238,238,235,774,233,231,233,233,231],8.4,[238,241,235,776,233,231,233,233,231],93.6,[238,247,235,778,233,231,233,233,231],93.7,[241,231,231,780,233,231,233,233,231],7.5,[241,235,231,782,233,231,233,233,231],3.7,[241,238,231,784,233,231,233,233,231],6.1,[241,241,231,786,233,231,233,233,231],89.2,[241,244,231,788,233,231,233,233,231],90.6,[241,231,235,790,233,231,233,233,231],13.7,[241,235,235,792,233,231,233,233,231],11.4,[241,238,235,794,233,231,233,233,231],12.6,[241,241,235,796,233,231,233,233,231],83.5,[241,247,235,798,233,231,233,233,231],82.3,[244,231,231,800,233,231,233,233,231],2.5,[244,235,231,743,233,231,233,233,231],[244,238,231,803,233,231,233,233,231],2.4,[244,241,231,805,233,231,233,233,231],96.3,[244,244,231,807,233,231,233,233,231],97.4,[244,231,235,809,233,231,233,233,231],9.6,[244,235,235,772,233,231,233,233,231],[244,238,235,812,233,231,233,233,231],8.2,[244,241,235,814,233,231,233,233,231],94.2,[244,247,235,730,233,231,233,233,231],[],[818],"Table V (VoR)",[],[],[822],"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":824,"group":825,"sourceId":826,"sourceLabel":827,"table":828,"selfRows":256,"metrics":829,"seqs":841,"entrants":848,"cells":859,"outcomes":939,"locators":940,"hardware":941,"wordings":942,"notes":943},"nerfloam2023-table-1","nerfloam2023:Table 1","nerfloam2023","Deng et al., 2023","Table 1",[830,833,835,837,839],{"label":831,"unit":832,"statistic":187,"alignment":187},"Map. Acc.","cm (not stated in this table; identical values are labelled cm in PIN-SLAM Table XI)",{"label":834,"unit":832,"statistic":187,"alignment":187},"Map. Comp.",{"label":836,"unit":832,"statistic":187,"alignment":187},"C-l1 (Chamfer-L1)",{"label":838,"unit":695,"statistic":187,"alignment":187},"F-score (10cm)",{"label":840,"unit":695,"statistic":187,"alignment":187},"F-score (20cm)",[842,845],{"dataset":843,"sequence":187,"environment":844},"MaiCity","synthetic urban street (simulated 64-beam LiDAR)",{"dataset":94,"sequence":846,"environment":847},"not_reported (Newer College sequence not named; one of every five scans used)","outdoor campus, hand-carried LiDAR",[849,851,853,855,857],{"name":850,"methodId":714,"linkable":73,"proposed":77,"self":77},"SHINE [50] with KissICP poses",{"name":852,"methodId":5,"linkable":73,"proposed":77,"self":73},"Vdbfusion [37] with KissICP poses",{"name":854,"methodId":826,"linkable":73,"proposed":73,"self":77},"Ours with KissICP poses",{"name":856,"methodId":711,"linkable":73,"proposed":77,"self":77},"Puma [36] with own odometry",{"name":858,"methodId":826,"linkable":73,"proposed":73,"self":77},"Ours with own odometry",[860,862,864,866,868,870,872,874,876,878,880,882,884,886,888,890,892,894,896,898,900,902,904,906,908,910,912,914,916,917,919,921,923,925,927,929,931,933,935,937],[231,231,231,861,233,231,233,233,231],5.75,[231,235,231,863,233,231,233,233,231],38.45,[231,238,231,865,233,231,233,233,231],22.1,[231,241,231,867,233,231,233,233,231],67,[231,231,235,869,233,231,233,233,231],14.87,[231,235,235,871,233,231,233,233,231],20.02,[231,238,235,873,233,231,233,233,231],17.45,[231,244,235,875,233,231,233,233,231],68.85,[235,231,231,877,233,231,233,233,231],4.95,[235,235,231,879,233,231,233,233,231],46.79,[235,238,231,881,233,231,233,233,231],25.87,[235,241,231,883,233,231,233,233,231],68.15,[235,231,235,885,233,231,233,233,231],14.03,[235,235,235,887,233,231,233,233,231],25.46,[235,238,235,889,233,231,233,233,231],19.75,[235,244,235,891,233,231,233,233,231],69.5,[238,231,231,893,233,231,233,233,231],4.16,[238,235,231,895,233,231,233,233,231],37.2,[238,238,231,897,233,231,233,233,231],20.67,[238,241,231,899,233,231,233,233,231],73.31,[238,231,235,901,233,231,233,233,231],14.31,[238,235,235,903,233,231,233,233,231],24.39,[238,238,235,905,233,231,233,233,231],19.35,[238,244,235,907,233,231,233,233,231],68.7,[241,231,231,909,233,231,233,233,231],7.89,[241,235,231,911,233,231,233,233,231],9.14,[241,238,231,913,233,231,233,233,231],8.51,[241,241,231,915,233,231,233,233,231],68.04,[241,231,235,411,233,231,233,233,231],[241,235,235,918,233,231,233,233,231],71.91,[241,238,235,920,233,231,233,233,231],43.6,[241,244,235,922,233,231,233,233,231],57.27,[244,231,231,924,233,231,233,233,231],5.69,[244,235,231,926,233,231,233,233,231],11.23,[244,238,231,928,233,231,233,233,231],8.46,[244,241,231,930,233,231,233,233,231],77.26,[244,231,235,932,233,231,233,233,231],12.89,[244,235,235,934,233,231,233,233,231],22.21,[244,238,235,936,233,231,233,233,231],17.55,[244,244,235,938,233,231,233,233,231],74.37,[],[828],[],[],[944],"Simultaneous odometry and mapping; SHINE-Mapping and VDBFusion are fed KISS-ICP poses, NeRF-LOAM is shown with KISS-ICP poses and with its own odometry, Puma uses its own odometry; same voxel size for all; Newer College uses one of every five scans; accuracy, completion and Chamfer-L1 units not stated here (the same Newer College values are labelled cm in PIN-SLAM Table XI)",[946,951,957,962,968,973,977,982,987,992],{"group":947,"slug":948,"sourceLabel":827,"table":949,"selfRows":256,"datasets":950},"nerfloam2023:Table 2","nerfloam2023-table-2","Table 2",[843,94],{"group":952,"slug":953,"sourceLabel":954,"table":955,"selfRows":256,"datasets":956},"pinslam2024:Table XI","pinslam2024-table-xi","Pan et al., 2024","Table XI",[94],{"group":958,"slug":959,"sourceLabel":6,"table":960,"selfRows":256,"datasets":961},"vizzo2022vdbfusion:Table 6","vizzo2022vdbfusion-table-6","Table 6",[120,89],{"group":963,"slug":964,"sourceLabel":965,"table":454,"selfRows":247,"datasets":966},"shinemapping2023:Table II","shinemapping2023-table-ii","Zhong et al., 2023",[967],"MaiCity (synthetic)",{"group":969,"slug":970,"sourceLabel":965,"table":971,"selfRows":247,"datasets":972},"shinemapping2023:Table III","shinemapping2023-table-iii","Table III",[94],{"group":974,"slug":975,"sourceLabel":6,"table":949,"selfRows":244,"datasets":976},"vizzo2022vdbfusion:Table 2","vizzo2022vdbfusion-table-2",[120,89],{"group":978,"slug":979,"sourceLabel":6,"table":980,"selfRows":244,"datasets":981},"vizzo2022vdbfusion:Table 3","vizzo2022vdbfusion-table-3","Table 3",[120,89],{"group":983,"slug":984,"sourceLabel":6,"table":985,"selfRows":244,"datasets":986},"vizzo2022vdbfusion:Table 5","vizzo2022vdbfusion-table-5","Table 5",[120,89],{"group":988,"slug":989,"sourceLabel":6,"table":990,"selfRows":238,"datasets":991},"vizzo2022vdbfusion:Table 4","vizzo2022vdbfusion-table-4","Table 4",[120,89],{"group":993,"slug":994,"sourceLabel":6,"table":995,"selfRows":235,"datasets":996},"vizzo2022vdbfusion:Text Sec.2","vizzo2022vdbfusion-text-sec-2","Text Sec.2",[94],1790510660576]