[{"data":1,"prerenderedAt":1412},["ShallowReactive",2],{"method-niceslam2022":3},{"method":4,"reference":52,"equipment":77,"figures":95,"results":96},{"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":26,"sensors":32,"platform":34,"estimator":35,"association":36,"timeModel":37,"deskew":38,"loopClosure":39,"globalOptimization":40,"mapRepresentation":41,"prior":42,"outputGeometry":43,"compute":44,"codeUrl":45,"codeLicense":46,"relatedVersions":47},"niceslam2022","Zhu et al., 2022a","NICE-SLAM","NICE-SLAM: Neural Implicit Scalable Encoding for SLAM",2022,"recent","C09","odometry_with_local_mapping","NICE-SLAM 以多層級特徵格網搭配預先訓練的小型解碼器取代單一 MLP，使地圖更新可局部進行，改善大型室內場景的可擴展性與過度平滑問題。追蹤與建圖以深度與顏色重渲染誤差交替最佳化。作者指出方法沒有迴圈閉合，且預測能力受限於粗網格尺度。","Hierarchical feature grids with pre-trained decoders enable local, scalable neural implicit RGB-D SLAM, without loop closure.","full_text_reviewed","peer_reviewed_published","main_body","論文未涉及營建場域；資料為 Replica、ScanNet、TUM RGB-D、Co-Fusion 與自錄公寓。",[20,21,22],"simulation","public_benchmark","controlled_experiment",[24,25],"More scalable and detailed than iMAP on large indoor scenes (abstract)","Can fill small holes and extrapolate unobserved geometry (Sec. 5)",[27,28,29,30,31],"No loop closure (Sec. 5)","Predictive ability limited to the coarse-grid scale (Sec. 5)","Tracking on TUM RGB-D still behind BAD-SLAM and ORB-SLAM2 (Table 2; Sec. 4.2)","Colour is only locally consistent because of forgetting; a global colour optimization is needed as post-processing to visualise the whole scene (Sec. 3.1)","Under dynamic objects, pixels with large re-rendering loss are only filtered; joint camera and scene optimization in dynamic scenes is left to future work (Sec. 3.3)",[33],"RGB-D",[],"alternating gradient-based optimization in parallel threads: staged mapping (mid-level grid, then mid and fine grids with the depth L1 loss) followed by a local bundle adjustment that jointly optimizes all feature grids, the colour decoder and the poses of K selected keyframes (Eq. 10); tracking optimizes only the current camera pose with a variance-weighted depth loss plus a photometric loss (Eq. 11 and 12)","direct depth (L1) and photometric re-rendering losses","discrete poses","not_applicable","none (authors list loop closure as future work)","none","hierarchical coarse\u002Fmid\u002Ffine feature grids with pre-trained occupancy decoders plus a colour grid","coarse, mid and fine occupancy decoders pre-trained as part of ConvONet on its Synthetic Indoor Scene Dataset (room_grid64 setting, point-cloud encoder) and kept fixed during SLAM; the colour decoder is optimized online","mesh via marching cubes: fine-level decoder occupancy for observed points; for unseen points inside partially observed coarse voxels the coarse decoder predicts occupancy (shown in cyan); other points set to zero occupancy","desktop PC with a 3.80 GHz Intel i7-10700K CPU and an NVIDIA RTX 3090 GPU (Sec. 4.1); Table 4 reports 47 ms tracking and 130 ms mapping at Mt = 200 and M = 1000 pixel samples (per iteration or per frame not stated) and 104.16 x10^3 FLOPs per point query; map memory 12.02 MB on Replica (Table 1). Independent measurements: [pointslam2023] Table 6 reports 1.32 s tracking and 10.92 s mapping per frame on Replica office 0 (RTX 2080 Ti); [photoslam2024] Table 1 reports 2.331 tracking FPS and more than 10 min operation time on Replica RGB-D (RTX 4090)","https:\u002F\u002Fgithub.com\u002Fcvg\u002Fnice-slam","Apache-2.0",[48],{"relation":49,"title":50,"doi_or_url":51},"preprint","arXiv:2112.12130","https:\u002F\u002Farxiv.org\u002Fabs\u002F2112.12130",{"id":5,"kind":53,"shortName":7,"title":8,"authors":54,"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":38,"codeUrl":45,"cluster":11,"topics":72,"mdpi":73,"verification":74,"label":6,"fulltextRoute":75,"versionRead":76,"addedByCensus":73},"method",[55,56,57,58,59,60,61,62],"Zihan Zhu","Songyou Peng","Viktor Larsson","Weiwei Xu","Hujun Bao","Zhaopeng Cui","Martin R. Oswald","Marc Pollefeys","2022 IEEE\u002FCVF Conference on Computer Vision and Pattern Recognition (CVPR)","conference","IEEE","pp. 12776-12786","10.1109\u002Fcvpr52688.2022.01245","2112.12130","https:\u002F\u002Fapi.crossref.org\u002Fworks\u002F10.1109\u002Fcvpr52688.2022.01245","2021-12-22","metadata_verified",[11],false,"confirmed","arXiv","arXiv v2 (2022-04-21) including the supplementary material (App. A and B); CVF open-access CVPR 2022 version also read for the main paper, with Tables 1 to 5, hardware and limitation text identical to arXiv v2",[78,85,88],{"category":79,"model":80,"canonical":80,"role":81,"dataset":82,"specs":83,"locator":84},"compute","Intel i7-10700K CPU (3.80 GHz)","compute for runtime",null,"desktop PC used for all NICE-SLAM runs","Sec. 4.1",{"category":79,"model":86,"canonical":86,"role":81,"dataset":82,"specs":87,"locator":84},"NVIDIA RTX 3090","GPU of the desktop PC used for all NICE-SLAM runs",{"category":89,"model":90,"canonical":82,"role":91,"dataset":92,"specs":93,"locator":94},"rgbd","not_reported","method input","self-captured multi-room apartment","sensor of the self-captured sequence in a large multi-room apartment is not named","Sec. 4.1; Sec. 4.2 Evaluation on a Larger Scene",[],{"totalRows":97,"groupCount":98,"groups":99,"others":1089},429,73,[100,374,580,907],{"slug":101,"group":102,"sourceId":103,"sourceLabel":104,"table":105,"selfRows":106,"metrics":107,"seqs":114,"entrants":135,"cells":150,"outcomes":368,"locators":369,"hardware":370,"wordings":371,"notes":372},"nerfslam2023-table-i","nerfslam2023:Table I","nerfslam2023","Rosinol et al., 2023","Table I",36,[108,111],{"label":109,"unit":110,"statistic":90,"alignment":38},"Depth L1 [cm]","cm",{"label":112,"unit":113,"statistic":90,"alignment":38},"PSNR [dB]","dB",[115,119,121,123,125,127,129,131,133],{"dataset":116,"sequence":117,"environment":118},"Replica","room-0","synthetic renders of scanned indoor scenes",{"dataset":116,"sequence":120,"environment":118},"room-1",{"dataset":116,"sequence":122,"environment":118},"room-2",{"dataset":116,"sequence":124,"environment":118},"office-0",{"dataset":116,"sequence":126,"environment":118},"office-1",{"dataset":116,"sequence":128,"environment":118},"office-2",{"dataset":116,"sequence":130,"environment":118},"office-3",{"dataset":116,"sequence":132,"environment":118},"office-4",{"dataset":116,"sequence":134,"environment":118},"average of 8 scenes",[136,140,142,144,146,148],{"name":137,"methodId":138,"linkable":139,"proposed":73,"self":73},"iMAP* [27] (GT depth)","imap2021",true,{"name":141,"methodId":5,"linkable":139,"proposed":73,"self":139},"Nice-SLAM [28] (GT depth)",{"name":143,"methodId":82,"linkable":73,"proposed":73,"self":73},"TSDF-Fusion Res. = 256 (our depth)",{"name":145,"methodId":82,"linkable":73,"proposed":73,"self":73},"sigma-Fusion [15] Res. = 256 (our depth)",{"name":147,"methodId":5,"linkable":139,"proposed":73,"self":139},"Nice-SLAM [28] (no depth)",{"name":149,"methodId":82,"linkable":73,"proposed":139,"self":73},"Ours (our depth)",[151,155,158,161,164,167,170,173,176,179,181,183,185,187,189,191,193,195,197,199,201,203,205,207,209,211,213,215,217,219,221,223,225,227,229,231,233,235,237,239,241,243,245,247,249,251,253,255,257,259,261,263,265,267,269,271,273,275,277,279,281,283,285,287,288,289,290,291,292,293,295,296,298,300,302,304,305,307,309,311,313,315,317,319,321,323,325,327,329,331,333,335,337,339,341,343,345,346,348,350,352,354,356,358,360,362,364,366],[152,152,152,153,154,152,154,154,152],0,5.7,-1,[152,152,156,157,154,152,154,154,152],1,4.93,[152,152,159,160,154,152,154,154,152],2,6.94,[152,152,162,163,154,152,154,154,152],3,6.43,[152,152,165,166,154,152,154,154,152],4,7.41,[152,152,168,169,154,152,154,154,152],5,14.23,[152,152,171,172,154,152,154,154,152],6,8.68,[152,152,174,175,154,152,154,154,152],7,6.8,[152,152,177,178,154,152,154,154,152],8,7.64,[152,156,152,180,154,152,154,154,152],5.66,[152,156,156,182,154,152,154,154,152],5.31,[152,156,159,184,154,152,154,154,152],5.64,[152,156,162,186,154,152,154,154,152],7.39,[152,156,165,188,154,152,154,154,152],11.89,[152,156,168,190,154,152,154,154,152],8.12,[152,156,171,192,154,152,154,154,152],5.62,[152,156,174,194,154,152,154,154,152],5.98,[152,156,177,196,154,152,154,154,152],6.95,[156,152,152,198,154,152,154,154,152],2.53,[156,152,156,200,154,152,154,154,152],3.45,[156,152,159,202,154,152,154,154,152],2.93,[156,152,162,204,154,152,154,154,152],1.51,[156,152,165,206,154,152,154,154,152],0.93,[156,152,168,208,154,152,154,154,152],8.41,[156,152,171,210,154,152,154,154,152],10.48,[156,152,174,212,154,152,154,154,152],2.43,[156,152,177,214,154,152,154,154,152],4.08,[156,156,152,216,154,152,154,154,152],29.9,[156,156,156,218,154,152,154,154,152],29.12,[156,156,159,220,154,152,154,154,152],19.8,[156,156,162,222,154,152,154,154,152],22.44,[156,156,165,224,154,152,154,154,152],25.22,[156,156,168,226,154,152,154,154,152],22.79,[156,156,171,228,154,152,154,154,152],22.94,[156,156,174,230,154,152,154,154,152],24.72,[156,156,177,232,154,152,154,154,152],24.61,[159,152,152,234,154,152,154,154,152],23.51,[159,152,156,236,154,152,154,154,152],20.94,[159,152,159,238,154,152,154,154,152],23.34,[159,152,162,240,154,152,154,154,152],14.11,[159,152,165,242,154,152,154,154,152],10.5,[159,152,168,244,154,152,154,154,152],30.89,[159,152,171,246,154,152,154,154,152],28.92,[159,152,174,248,154,152,154,154,152],22.83,[159,152,177,250,154,152,154,154,152],21.88,[159,156,152,252,154,152,154,154,152],3.43,[159,156,156,254,154,152,154,154,152],4.51,[159,156,159,256,154,152,154,154,152],5.57,[159,156,162,258,154,152,154,154,152],11.16,[159,156,165,260,154,152,154,154,152],15.92,[159,156,168,262,154,152,154,154,152],4.86,[159,156,171,264,154,152,154,154,152],5.68,[159,156,174,266,154,152,154,154,152],5.46,[159,156,177,268,154,152,154,154,152],7.07,[162,152,152,270,154,152,154,154,152],21.92,[162,152,156,272,154,152,154,154,152],19.28,[162,152,159,274,154,152,154,154,152],22.4,[162,152,162,276,154,152,154,154,152],13.8,[162,152,165,278,154,152,154,154,152],10.21,[162,152,168,280,154,152,154,154,152],22.27,[162,152,171,282,154,152,154,154,152],28.7,[162,152,174,284,154,152,154,154,152],22.21,[162,152,177,286,154,152,154,154,152],20.1,[162,156,152,200,154,152,154,154,152],[162,156,156,254,154,152,154,154,152],[162,156,159,256,154,152,154,154,152],[162,156,162,258,154,152,154,154,152],[162,156,165,260,154,152,154,154,152],[162,156,168,262,154,152,154,154,152],[162,156,171,294,154,152,154,154,152],5.69,[162,156,174,266,154,152,154,154,152],[162,156,177,297,154,152,154,154,152],7.08,[165,152,152,299,154,152,154,154,152],11.12,[165,152,156,301,154,152,154,154,152],9.42,[165,152,159,303,154,152,154,154,152],19.03,[165,152,162,299,154,152,154,154,152],[165,152,165,306,154,152,154,154,152],10.24,[165,152,168,308,154,152,154,154,152],16.36,[165,152,171,310,154,152,154,154,152],21.33,[165,152,174,312,154,152,154,154,152],14.81,[165,152,177,314,154,152,154,154,152],14.18,[165,156,152,316,154,152,154,154,152],18.15,[165,156,156,318,154,152,154,154,152],18.22,[165,156,159,320,154,152,154,154,152],17.82,[165,156,162,322,154,152,154,154,152],20.23,[165,156,165,324,154,152,154,154,152],19.14,[165,156,168,326,154,152,154,154,152],15.22,[165,156,171,328,154,152,154,154,152],16.12,[165,156,174,330,154,152,154,154,152],17.24,[165,156,177,332,154,152,154,154,152],17.76,[168,152,152,334,154,152,154,154,152],8.11,[168,152,156,336,154,152,154,154,152],5.06,[168,152,159,338,154,152,154,154,152],4.72,[168,152,162,340,154,152,154,154,152],4.58,[168,152,165,342,154,152,154,154,152],16.32,[168,152,168,344,154,152,154,154,152],14.14,[168,152,171,314,154,152,154,154,152],[168,152,174,347,154,152,154,154,152],7.19,[168,152,177,349,154,152,154,154,152],9.29,[168,156,152,351,154,152,154,154,152],34.09,[168,156,156,353,154,152,154,154,152],37.52,[168,156,159,355,154,152,154,154,152],41.9,[168,156,162,357,154,152,154,154,152],50.62,[168,156,165,359,154,152,154,154,152],53.21,[168,156,168,361,154,152,154,154,152],39.6,[168,156,171,363,154,152,154,154,152],39.74,[168,156,174,365,154,152,154,154,152],39.53,[168,156,177,367,154,152,154,154,152],42.03,[],[105],[],[],[373],"Replica rendered sequences (2000 frames per scene from iMAP); iMAP* and NICE-SLAM (GT depth) use rendered ground-truth depth; TSDF-Fusion, sigma-Fusion and NeRF-SLAM use DROID-SLAM poses and depths; Depth L1 is a proxy for geometric accuracy; values from the IROS 2023 version of record (the NeRF-SLAM row differs from arXiv v1)",{"slug":375,"group":376,"sourceId":377,"sourceLabel":378,"table":379,"selfRows":380,"metrics":381,"seqs":390,"entrants":409,"cells":416,"outcomes":574,"locators":575,"hardware":576,"wordings":577,"notes":578},"voxfusion2022-table-2","voxfusion2022:Table 2","voxfusion2022","Yang et al., 2022","Table 2",27,[382,385,387],{"label":383,"unit":110,"statistic":384,"alignment":38},"Acc. [cm]","mean",{"label":386,"unit":110,"statistic":384,"alignment":38},"Comp. [cm]",{"label":388,"unit":389,"statistic":38,"alignment":38},"Comp. Ratio [ \u003C5 cm %]","%",[391,394,396,398,400,402,404,406,408],{"dataset":116,"sequence":392,"environment":393},"Room-0","synthetic indoor scenes",{"dataset":116,"sequence":395,"environment":393},"Room-1",{"dataset":116,"sequence":397,"environment":393},"Room-2",{"dataset":116,"sequence":399,"environment":393},"Office-0",{"dataset":116,"sequence":401,"environment":393},"Office-1",{"dataset":116,"sequence":403,"environment":393},"Office-2",{"dataset":116,"sequence":405,"environment":393},"Office-3",{"dataset":116,"sequence":407,"environment":393},"Office-4",{"dataset":116,"sequence":134,"environment":393},[410,412,414],{"name":411,"methodId":138,"linkable":139,"proposed":73,"self":73},"iMap [ 31 ]",{"name":413,"methodId":5,"linkable":139,"proposed":73,"self":139},"NICE-SLAM [ 40 ]",{"name":415,"methodId":82,"linkable":73,"proposed":139,"self":73},"Ours",[417,419,421,423,425,427,429,431,433,435,436,438,440,442,444,446,448,450,452,454,456,458,460,462,464,466,468,470,472,474,476,477,479,481,483,484,486,488,490,492,494,496,498,500,502,503,505,507,509,511,513,515,517,519,521,523,525,527,529,531,533,535,537,539,541,542,544,546,548,550,552,554,556,558,560,562,564,566,568,570,572],[152,152,152,418,154,152,154,154,152],3.58,[152,152,156,420,154,152,154,154,152],3.69,[152,152,159,422,154,152,154,154,152],4.68,[152,152,162,424,154,152,154,154,152],5.87,[152,152,165,426,154,152,154,154,152],3.71,[152,152,168,428,154,152,154,154,152],4.81,[152,152,171,430,154,152,154,154,152],4.27,[152,152,174,432,154,152,154,154,152],4.83,[152,152,177,434,154,152,154,154,152],4.43,[152,156,152,336,154,152,154,154,152],[152,156,156,437,154,152,154,154,152],4.87,[152,156,159,439,154,152,154,154,152],5.51,[152,156,162,441,154,152,154,154,152],6.11,[152,156,165,443,154,152,154,154,152],5.26,[152,156,168,445,154,152,154,154,152],5.65,[152,156,171,447,154,152,154,154,152],5.45,[152,156,174,449,154,152,154,154,152],6.59,[152,156,177,451,154,152,154,154,152],5.56,[152,159,152,453,154,152,154,154,152],83.91,[152,159,156,455,154,152,154,154,152],83.45,[152,159,159,457,154,152,154,154,152],75.53,[152,159,162,459,154,152,154,154,152],77.71,[152,159,165,461,154,152,154,154,152],79.64,[152,159,168,463,154,152,154,154,152],77.22,[152,159,171,465,154,152,154,154,152],77.34,[152,159,174,467,154,152,154,154,152],77.63,[152,159,177,469,154,152,154,154,152],79.06,[156,152,152,471,154,152,154,154,152],3.53,[156,152,156,473,154,152,154,154,152],3.6,[156,152,159,475,154,152,154,154,152],3.03,[156,152,162,451,154,152,154,154,152],[156,152,165,478,154,152,154,154,152],3.35,[156,152,168,480,154,152,154,154,152],4.71,[156,152,171,482,154,152,154,154,152],3.84,[156,152,174,478,154,152,154,154,152],[156,152,177,485,154,152,154,154,152],3.87,[156,156,152,487,154,152,154,154,152],3.4,[156,156,156,489,154,152,154,154,152],3.62,[156,156,159,491,154,152,154,154,152],3.27,[156,156,162,493,154,152,154,154,152],4.55,[156,156,165,495,154,152,154,154,152],4.03,[156,156,168,497,154,152,154,154,152],3.94,[156,156,171,499,154,152,154,154,152],3.99,[156,156,174,501,154,152,154,154,152],4.15,[156,156,177,485,154,152,154,154,152],[156,159,152,504,154,152,154,154,152],86.05,[156,159,156,506,154,152,154,154,152],80.75,[156,159,159,508,154,152,154,154,152],87.23,[156,159,162,510,154,152,154,154,152],79.34,[156,159,165,512,154,152,154,154,152],82.13,[156,159,168,514,154,152,154,154,152],80.35,[156,159,171,516,154,152,154,154,152],80.55,[156,159,174,518,154,152,154,154,152],82.88,[156,159,177,520,154,152,154,154,152],82.41,[159,152,152,522,154,152,154,154,152],2.41,[159,152,156,524,154,152,154,154,152],1.62,[159,152,159,526,154,152,154,154,152],3.11,[159,152,162,528,154,152,154,154,152],1.74,[159,152,165,530,154,152,154,154,152],1.69,[159,152,168,532,154,152,154,154,152],2.23,[159,152,171,534,154,152,154,154,152],2.84,[159,152,174,536,154,152,154,154,152],3.31,[159,152,177,538,154,152,154,154,152],2.37,[159,156,152,540,154,152,154,154,152],2.6,[159,156,156,532,154,152,154,154,152],[159,156,159,543,154,152,154,154,152],1.93,[159,156,162,545,154,152,154,154,152],1.39,[159,156,165,547,154,152,154,154,152],1.8,[159,156,168,549,154,152,154,154,152],2.71,[159,156,171,551,154,152,154,154,152],2.69,[159,156,174,553,154,152,154,154,152],2.88,[159,156,177,555,154,152,154,154,152],2.28,[159,159,152,557,154,152,154,154,152],92.87,[159,159,156,559,154,152,154,154,152],93.48,[159,159,159,561,154,152,154,154,152],94.34,[159,159,162,563,154,152,154,154,152],97.21,[159,159,165,565,154,152,154,154,152],93.76,[159,159,168,567,154,152,154,154,152],90.98,[159,159,171,569,154,152,154,154,152],90.73,[159,159,174,571,154,152,154,154,152],89.48,[159,159,177,573,154,152,154,154,152],92.86,[],[379],[],[],[579],"Replica mesh reconstruction; iMAP values from its paper, NICE-SLAM values from its supplementary without mesh culling",{"slug":581,"group":582,"sourceId":583,"sourceLabel":584,"table":585,"selfRows":586,"metrics":587,"seqs":591,"entrants":638,"cells":662,"outcomes":901,"locators":902,"hardware":903,"wordings":904,"notes":905},"splatam2024-table-1","splatam2024:Table 1","splatam2024","Keetha et al., 2024","Table 1",22,[588],{"label":589,"unit":110,"statistic":590,"alignment":90},"ATE RMSE [cm]","RMSE",[592,595,597,599,601,603,605,607,609,611,614,616,618,620,622,624,626,628,630,632,634,636],{"dataset":116,"sequence":593,"environment":594},"Avg.","synthetic scenes",{"dataset":116,"sequence":596,"environment":594},"room0",{"dataset":116,"sequence":598,"environment":594},"room1",{"dataset":116,"sequence":600,"environment":594},"room2",{"dataset":116,"sequence":602,"environment":594},"office0",{"dataset":116,"sequence":604,"environment":594},"office1",{"dataset":116,"sequence":606,"environment":594},"office2",{"dataset":116,"sequence":608,"environment":594},"office3",{"dataset":116,"sequence":610,"environment":594},"office4",{"dataset":612,"sequence":593,"environment":613},"TUM-RGBD","real RGB-D sequences from old low-quality cameras (sparse depth, strong motion blur)",{"dataset":612,"sequence":615,"environment":613},"fr1\u002Fdesk",{"dataset":612,"sequence":617,"environment":613},"fr1\u002Fdesk2",{"dataset":612,"sequence":619,"environment":613},"fr1\u002Froom",{"dataset":612,"sequence":621,"environment":613},"fr2\u002Fxyz",{"dataset":612,"sequence":623,"environment":613},"fr3\u002Foffice",{"dataset":625,"sequence":593,"environment":613},"ScanNet 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,11.31,[156,152,765,812,154,152,154,154,152],3.52,[156,152,768,171,154,152,154,154,152],[156,152,771,815,154,152,154,154,152],19.53,[156,152,774,817,154,152,154,154,152],1.49,[156,152,777,819,154,152,154,154,152],26.01,[165,152,762,821,154,152,154,154,152],8.92,[165,152,765,823,154,152,154,154,152],4.34,[165,152,768,825,154,152,154,154,152],4.54,[165,152,771,827,154,152,154,154,152],30.92,[165,152,774,703,154,152,154,154,152],[165,152,777,830,154,152,154,154,152],3.48,[168,152,762,832,154,152,154,154,152],5.48,[168,152,765,478,154,152,154,154,152],[168,152,768,835,154,152,154,154,152],6.54,[168,152,771,837,154,152,154,154,152],11.13,[168,152,774,839,154,152,154,154,152],1.24,[168,152,777,841,154,152,154,154,152],5.16,[156,152,843,844,154,152,154,154,152],15,26.9,[156,152,846,847,154,152,154,154,152],16,68.84,[156,152,849,850,154,152,154,154,152],17,24.18,[156,152,852,208,154,152,154,154,152],18,[156,152,854,855,154,152,154,154,152],19,27.28,[156,152,857,858,154,152,154,154,152],20,23.3,[156,152,860,861,154,152,154,154,152],21,9.41,[159,152,843,863,154,152,154,154,152],10.7,[159,152,846,771,154,152,154,154,152],[159,152,849,777,154,152,154,154,152],[159,152,852,867,154,152,154,154,152],7.9,[159,152,854,869,154,152,154,154,152],10.9,[159,152,857,871,154,152,154,154,152],13.4,[159,152,860,873,154,152,154,154,152],6.2,[165,152,843,875,154,152,154,154,152],12.19,[165,152,846,306,154,152,154,154,152],[165,152,849,878,154,152,154,154,152],7.81,[165,152,852,880,154,152,154,154,152],8.65,[165,152,854,882,154,152,154,154,152],22.16,[165,152,857,884,154,152,154,154,152],14.77,[165,152,860,886,154,152,154,154,152],9.54,[168,152,843,888,154,152,154,154,152],11.88,[168,152,846,890,154,152,154,154,152],12.83,[168,152,849,892,154,152,154,154,152],10.1,[168,152,852,894,154,152,154,154,152],17.72,[168,152,854,896,154,152,154,154,152],12.08,[168,152,857,898,154,152,154,154,152],11.1,[168,152,860,900,154,152,154,154,152],7.46,[],[585],[],[],[906],"Online camera-pose estimation, ATE RMSE [cm]; Baseline numbers taken from Point-SLAM; SplaTAM averaged over 3 seeds",{"slug":908,"group":909,"sourceId":910,"sourceLabel":911,"table":912,"selfRows":846,"metrics":913,"seqs":924,"entrants":938,"cells":949,"outcomes":1081,"locators":1084,"hardware":1085,"wordings":1086,"notes":1087},"loner2023-table-iii","loner2023:Table III","loner2023","Isaacson et al., 2023","Table III",[914,917,919,922],{"label":915,"unit":916,"statistic":384,"alignment":90},"Accuracy (mean distance estimated to ground truth)","m",{"label":918,"unit":916,"statistic":384,"alignment":90},"Completion (mean distance ground truth to estimated)",{"label":920,"unit":921,"statistic":90,"alignment":90},"Precision at 0.1 m threshold","ratio",{"label":923,"unit":921,"statistic":90,"alignment":90},"Recall at 0.1 m threshold",[925,929,932,934],{"dataset":926,"sequence":927,"environment":928},"Fusion Portable","MCR Slow 01","indoor lab, quadruped",{"dataset":926,"sequence":930,"environment":931},"Canteen Day","semi-outdoor courtyard, handheld",{"dataset":926,"sequence":933,"environment":931},"Garden Day",{"dataset":935,"sequence":936,"environment":937},"Newer College","Quad Easy","large outdoor college quad, two laps",[939,940,943,945,947],{"name":7,"methodId":5,"linkable":139,"proposed":73,"self":139},{"name":941,"methodId":942,"linkable":139,"proposed":73,"self":73},"SHINE (ground-truth poses)","shinemapping2023",{"name":944,"methodId":82,"linkable":73,"proposed":73,"self":73},"LONER w.\u002F L_CLONeR",{"name":946,"methodId":82,"linkable":73,"proposed":73,"self":73},"LONER w.\u002F L_URF",{"name":948,"methodId":910,"linkable":139,"proposed":139,"self":73},"LONER",[950,952,954,956,958,960,962,964,966,968,970,972,974,976,978,980,982,984,986,988,990,991,992,993,994,995,996,998,1000,1002,1004,1005,1006,1007,1008,1009,1010,1012,1014,1016,1018,1019,1020,1021,1022,1023,1024,1026,1028,1030,1032,1033,1034,1035,1036,1037,1038,1040,1042,1044,1046,1047,1049,1051,1053,1054,1055,1057,1059,1061,1063,1064,1066,1068,1070,1072,1073,1075,1077,1079],[152,152,152,951,154,152,154,154,152],0.621,[156,152,152,953,154,152,154,154,152],0.164,[159,152,152,955,154,152,154,154,152],0.11,[162,152,152,957,154,152,154,154,152],0.153,[165,152,152,959,154,152,154,154,152],0.186,[152,156,152,961,154,152,154,154,152],0.419,[156,156,152,963,154,152,154,154,152],0.075,[159,156,152,965,154,152,154,154,152],0.08,[162,156,152,967,154,152,154,154,152],0.102,[165,156,152,969,154,152,154,154,152],0.069,[152,159,152,971,154,152,154,154,152],0.124,[156,159,152,973,154,152,154,154,152],0.624,[159,159,152,975,154,152,154,154,152],0.665,[162,159,152,977,154,152,154,154,152],0.449,[165,159,152,979,154,152,154,154,152],0.473,[152,162,152,981,154,152,154,154,152],0.476,[156,162,152,983,154,152,154,154,152],0.757,[159,162,152,985,154,152,154,154,152],0.94,[162,162,152,987,154,152,154,154,152],0.884,[165,162,152,989,154,152,154,154,152],0.932,[152,152,156,82,152,152,154,154,152],[156,152,156,82,156,152,154,154,152],[159,152,156,82,156,152,154,154,152],[162,152,156,82,156,152,154,154,152],[165,152,156,82,156,152,154,154,152],[152,156,156,82,152,152,154,154,152],[156,156,156,997,154,152,154,154,152],0.116,[159,156,156,999,154,152,154,154,152],0.22,[162,156,156,1001,154,152,154,154,152],0.19,[165,156,156,1003,154,152,154,154,152],0.105,[152,159,156,82,152,152,154,154,152],[156,159,156,82,156,152,154,154,152],[159,159,156,82,156,152,154,154,152],[162,159,156,82,156,152,154,154,152],[165,159,156,82,156,152,154,154,152],[152,162,156,82,152,152,154,154,152],[156,162,156,1011,154,152,154,154,152],0.753,[159,162,156,1013,154,152,154,154,152],0.524,[162,162,156,1015,154,152,154,154,152],0.846,[165,162,156,1017,154,152,154,154,152],0.878,[152,152,159,82,152,152,154,154,152],[156,152,159,82,156,152,154,154,152],[159,152,159,82,156,152,154,154,152],[162,152,159,82,156,152,154,154,152],[165,152,159,82,156,152,154,154,152],[152,156,159,82,152,152,154,154,152],[156,156,159,1025,154,152,154,154,152],0.13,[159,156,159,1027,154,152,154,154,152],0.333,[162,156,159,1029,154,152,154,154,152],0.539,[165,156,159,1031,154,152,154,154,152],0.157,[152,159,159,82,152,152,154,154,152],[156,159,159,82,156,152,154,154,152],[159,159,159,82,156,152,154,154,152],[162,159,159,82,156,152,154,154,152],[165,159,159,82,156,152,154,154,152],[152,162,159,82,152,152,154,154,152],[156,162,159,1039,154,152,154,154,152],0.657,[159,162,159,1041,154,152,154,154,152],0.469,[162,162,159,1043,154,152,154,154,152],0.623,[165,162,159,1045,154,152,154,154,152],0.784,[152,152,162,82,156,152,154,154,152],[156,152,162,1048,154,152,154,154,152],0.301,[159,152,162,1050,154,152,154,154,152],0.663,[162,152,162,1052,154,152,154,154,152],0.552,[165,152,162,664,154,152,154,154,152],[152,156,162,82,156,152,154,154,152],[156,156,162,1056,154,152,154,154,152],0.148,[159,156,162,1058,154,152,154,154,152],0.543,[162,156,162,1060,154,152,154,154,152],0.895,[165,156,162,1062,154,152,154,154,152],0.373,[152,159,162,82,156,152,154,154,152],[156,159,162,1065,154,152,154,154,152],0.453,[159,159,162,1067,154,152,154,154,152],0.15,[162,159,162,1069,154,152,154,154,152],0.127,[165,159,162,1071,154,152,154,154,152],0.327,[152,162,162,82,156,152,154,154,152],[156,162,162,1074,154,152,154,154,152],0.717,[159,162,162,1076,154,152,154,154,152],0.602,[162,162,162,1078,154,152,154,154,152],0.484,[165,162,162,1080,154,152,154,154,152],0.809,[1082,1083],"failed","not_applicable (invalid configuration '-')",[912],[],[],[1088],"Map accuracy and completion (m, mean nearest-point distances) and precision and recall at a 0.1 m threshold; meshes from each method sampled to point clouds, all clouds voxel-downsampled to 5 cm (1 cm for MCR), ground truth cropped to geometry observed by the sensor. SHINE Mapping used ground-truth poses. '-' = invalid configuration (accuracy and precision are '-' for every method on Canteen and Garden; NICE-SLAM '-' on Quad), x = failed (NICE-SLAM on Canteen and Garden, all metrics).",[1090,1097,1103,1109,1114,1119,1124,1129,1134,1138,1144,1149,1154,1159,1164,1168,1172,1177,1182,1187,1191,1195,1200,1204,1209,1215,1219,1223,1227,1232,1236,1240,1244,1248,1252,1256,1261,1266,1270,1274,1279,1283,1287,1292,1296,1301,1305,1309,1313,1317,1321,1325,1329,1334,1339,1344,1348,1352,1357,1364,1368,1372,1376,1381,1385,1389,1395,1401,1407],{"group":1091,"slug":1092,"sourceLabel":1093,"table":585,"selfRows":777,"datasets":1094},"coslam2023:Table 1","coslam2023-table-1","Wang et al., 2023a",[1095,1096],"Replica (8 synthetic scenes)","Synthetic RGB-D of NeuralRGBD (7 scenes, simulated depth noise)",{"group":1098,"slug":1099,"sourceLabel":1100,"table":379,"selfRows":777,"datasets":1101},"eslam2023:Table 2","eslam2023-table-2","Johari et al., 2023",[1102],"ScanNet",{"group":1104,"slug":1105,"sourceLabel":1106,"table":1107,"selfRows":762,"datasets":1108},"goslam2023:Table 3","goslam2023-table-3","Zhang et al., 2023b","Table 3",[1102],{"group":1110,"slug":1111,"sourceLabel":1112,"table":585,"selfRows":762,"datasets":1113},"gsicpslam2024:Table 1","gsicpslam2024-table-1","Ha et al., 2024",[116],{"group":1115,"slug":1116,"sourceLabel":1117,"table":585,"selfRows":762,"datasets":1118},"gsslam2024:Table 1","gsslam2024-table-1","Yan et al., 2024",[116],{"group":1120,"slug":1121,"sourceLabel":1122,"table":379,"selfRows":762,"datasets":1123},"monogs2024:Table 2","monogs2024-table-2","Matsuki et al., 2024",[116],{"group":1125,"slug":1126,"sourceLabel":1127,"table":1107,"selfRows":762,"datasets":1128},"nicerslam2024:Table 3","nicerslam2024-table-3","Zhu et al., 2024",[116],{"group":1130,"slug":1131,"sourceLabel":1132,"table":585,"selfRows":762,"datasets":1133},"pointslam2023:Table 1","pointslam2023-table-1","Sandström et al., 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