[{"data":1,"prerenderedAt":1280},["ShallowReactive",2],{"method-splatam2024":3},{"method":4,"reference":51,"equipment":75,"figures":96,"results":126},{"id":5,"label":6,"shortName":7,"title":8,"year":9,"era":10,"cluster":11,"scope":12,"keyIdeaZh":13,"keyIdeaEn":14,"fulltextStatus":15,"publicationStatus":16,"recommendation":17,"constructionRelevance":18,"validationEnvironment":19,"strengths":22,"limitations":25,"sensors":32,"platform":34,"estimator":35,"association":36,"timeModel":37,"deskew":38,"loopClosure":39,"globalOptimization":39,"mapRepresentation":40,"prior":41,"outputGeometry":42,"compute":43,"codeUrl":44,"codeLicense":45,"relatedVersions":46},"splatam2024","Keetha et al., 2024","SplaTAM","SplaTAM: Splat, Track & Map 3D Gaussians for Dense RGB-D SLAM",2024,"recent","C09","odometry_with_local_mapping","SplaTAM 以等向性、顏色不隨視角變化的三維高斯為唯一地圖。追蹤時固定高斯，只在剪影值大於 0.99 的已充分觀測像素上，以深度 L1 與權重減半的顏色 L1 最佳化位姿（以等速模型初始化）；建圖時依剪影與深度誤差新增高斯，並以重疊度最高的關鍵影格更新地圖。高斯初始位置直接取自 RGB-D 深度反投影，故幾何來源是量測深度。全文與補充資料都沒有三維表面精度指標，幾何只以渲染深度 L1 評估（ScanNet++ 新視角 2.07 cm、訓練視角 1.28 cm）。Replica、TUM-RGBD 與 ScanNet 的基準數值取自 Point-SLAM 論文而非重跑；在 TUM-RGBD 上平均 ATE 5.48 cm，仍不及特徵式 ORB-SLAM2 的 1.98 cm。在 RTX 3080 Ti 上每影格追蹤約 1.00 s、建圖約 1.44 s，並非即時。","Dense RGB-D SLAM with isotropic 3D Gaussians, silhouette-guided tracking and densification, evaluated by rendering, depth L1 and ATE.","full_text_reviewed","peer_reviewed_published","main_body","論文未涉及營建場域；資料為 ScanNet++、Replica、TUM-RGBD、ScanNet。",[20,21],"simulation","public_benchmark",[23,24],"Sub-centimetre localization claims in texture-less real scenes (Fig. 1 caption)","Faster optimization than implicit representations thanks to rasterization (Sec. 1)",[26,27,28,29,30,31],"Sensitive to motion blur, large depth noise and aggressive rotation (Limitations)","Requires known intrinsics and dense depth (Limitations)","Scaling to large scenes left to future work (Limitations)","Feature-based ORB-SLAM2 outperforms SplaTAM on TUM-RGBD (1.98 vs 5.48 cm average ATE) and no dense method reaches below 10 cm on original ScanNet (Table 1; Sec. 5)","Replica, TUM-RGBD and ScanNet baseline numbers are copied from Point-SLAM rather than rerun (Sec. 4; Table 1 caption)","(inference) Not real time: about 1.00 s tracking plus 1.44 s mapping per frame on an RTX 3080 Ti (Table 6)",[33],"RGB-D",[],"gradient-based camera pose optimization through differentiable splatting (constant-velocity initialization); map update over overlapping keyframes","direct L1 depth + colour rendering losses on silhouette-masked (well-observed) pixels","discrete poses","not_applicable","none","isotropic 3D Gaussians with view-independent colour","none; Gaussians initialized by unprojecting sensor depth","Gaussian map with rendered RGB and depth; geometry assessed only through rendered depth L1 against ground-truth depth (ScanNet++ 2.07 cm on novel views, 1.28 cm on training views); the full paper and supplement contain no 3D surface metric","RTX 3080 Ti: 1.00 s tracking and 1.44 s mapping per frame on Replica room0 (40 and 60 iterations); SplaTAM-S 0.19 s and 0.33 s (Table 6); rendering up to 400 FPS at 876x584 (Fig. 1)","https:\u002F\u002Fgithub.com\u002Fspla-tam\u002FSplaTAM","BSD-3-Clause",[47],{"relation":48,"title":49,"doi_or_url":50},"preprint","arXiv:2312.02126","https:\u002F\u002Farxiv.org\u002Fabs\u002F2312.02126",{"id":5,"kind":52,"shortName":7,"title":8,"authors":53,"year":9,"venue":61,"venueType":62,"publisher":63,"volumeIssuePages":64,"doi":65,"arxivId":66,"url":67,"firstPublicDate":68,"publicationStatus":16,"metadataStatus":69,"fulltextStatus":15,"era":10,"classicReason":38,"codeUrl":44,"cluster":11,"topics":70,"mdpi":71,"verification":72,"label":6,"fulltextRoute":73,"versionRead":74,"addedByCensus":71},"method",[54,55,56,57,58,59,60],"Nikhil Keetha","Jay Karhade","Krishna Murthy Jatavallabhula","Gengshan Yang","Sebastian Scherer","Deva Ramanan","Jonathon Luiten","2024 IEEE\u002FCVF Conference on Computer Vision and Pattern Recognition (CVPR)","conference","IEEE","pp. 21357-21366","10.1109\u002Fcvpr52733.2024.02018","2312.02126","https:\u002F\u002Fapi.crossref.org\u002Fworks\u002F10.1109\u002Fcvpr52733.2024.02018","2023-12-04","metadata_verified",[11],false,"confirmed","arXiv","arXiv v3 (16 Apr 2024) including supplementary material; CVF open-access CVPR 2024 paper (same content as the IEEE version, without supplement) checked: Tables 1 to 4 and 6 identical",[76,83,90],{"category":77,"model":78,"canonical":78,"role":79,"dataset":80,"specs":81,"locator":82},"compute","NVIDIA RTX 3080 Ti","compute for runtime",null,"Runtime comparison on Replica room0","Table 6; Sec. 5",{"category":84,"model":85,"canonical":85,"role":86,"dataset":87,"specs":88,"locator":89},"camera","DSLR (model not stated in the paper)","dataset sensor","ScanNet++","ScanNet++ DSLR captures with complete dense trajectories and a second capture loop for novel views","Sec. 4",{"category":91,"model":92,"canonical":92,"role":93,"dataset":80,"specs":94,"locator":95},"rgbd","iPhone (commodity camera and time-of-flight sensor)","method input","Qualitative online reconstructions shown on the project website only; no quantitative evaluation","Supplementary S1",[97,110,116],{"refId":5,"refLabel":6,"fig":98,"whatZh":99,"license":100,"licenseUrl":101,"sourceUrl":102,"src":103,"width":104,"height":105,"thumb":106,"thumbWidth":107,"thumbHeight":108,"modified":109},"Fig. 1","SplaTAM 高斯地圖、訓練與新視角相機視錐，以及渲染與深度誤差示例","CC BY-SA 4.0 (arXiv v3)","http:\u002F\u002Fcreativecommons.org\u002Flicenses\u002Fby-sa\u002F4.0\u002F","https:\u002F\u002Farxiv.org\u002Fhtml\u002F2312.02126v3\u002Fsplash_fig.png","\u002Ffigure-files\u002Fsplatam2024\u002Ffig-1.webp",1400,788,"\u002Ffigure-files\u002Fsplatam2024\u002Ffig-1.thumb.webp",480,270,"resized to at most 1400 px wide and converted to WebP",{"refId":5,"refLabel":6,"fig":111,"whatZh":112,"license":100,"licenseUrl":101,"sourceUrl":113,"src":114,"width":104,"height":105,"thumb":115,"thumbWidth":107,"thumbHeight":108,"modified":109},"Fig. 2","SplaTAM 流程：剪影引導追蹤、高斯增密與地圖更新三步驟","https:\u002F\u002Farxiv.org\u002Fhtml\u002F2312.02126v3\u002Fpipeline_v4.png","\u002Ffigure-files\u002Fsplatam2024\u002Ffig-2.webp","\u002Ffigure-files\u002Fsplatam2024\u002Ffig-2.thumb.webp",{"refId":5,"refLabel":6,"fig":117,"whatZh":118,"license":100,"licenseUrl":101,"sourceUrl":119,"src":120,"width":121,"height":122,"thumb":123,"thumbWidth":107,"thumbHeight":124,"modified":125},"Fig. S.1","ScanNet++ S2 的重建結果與估計、真值相機位姿比對","https:\u002F\u002Farxiv.org\u002Fhtml\u002F2312.02126v3\u002Ffigs\u002Fscene2_v3.png","\u002Ffigure-files\u002Fsplatam2024\u002Ffig-s-1.webp",1200,680,"\u002Ffigure-files\u002Fsplatam2024\u002Ffig-s-1.thumb.webp",272,"converted to WebP",{"totalRows":127,"groupCount":128,"groups":129,"others":1148},163,29,[130,507,822,1002],{"slug":131,"group":132,"sourceId":5,"sourceLabel":6,"table":133,"selfRows":134,"metrics":135,"seqs":141,"entrants":195,"cells":224,"outcomes":499,"locators":501,"hardware":502,"wordings":503,"notes":504},"splatam2024-table-1","splatam2024:Table 1","Table 1",25,[136],{"label":137,"unit":138,"statistic":139,"alignment":140},"ATE RMSE [cm]","cm","RMSE","not_reported",[142,145,147,149,152,154,156,158,160,162,164,166,168,171,173,175,177,179,181,183,185,187,189,191,193],{"dataset":87,"sequence":143,"environment":144},"Avg.","high-quality DSLR colour and depth captures with very large inter-frame motion",{"dataset":87,"sequence":146,"environment":144},"S1 (8b5caf3398)",{"dataset":87,"sequence":148,"environment":144},"S2 (b20a261fdf)",{"dataset":150,"sequence":143,"environment":151},"Replica","synthetic 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blur)",{"dataset":169,"sequence":172,"environment":170},"fr1\u002Fdesk",{"dataset":169,"sequence":174,"environment":170},"fr1\u002Fdesk2",{"dataset":169,"sequence":176,"environment":170},"fr1\u002Froom",{"dataset":169,"sequence":178,"environment":170},"fr2\u002Fxyz",{"dataset":169,"sequence":180,"environment":170},"fr3\u002Foffice",{"dataset":182,"sequence":143,"environment":170},"ScanNet 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(tracking failure reported in Sec. 5)",[133],[],[],[505,506],"Online camera-pose estimation, ATE RMSE [cm]; ScanNet++ baselines run by the authors; Point-SLAM and ORB-SLAM3 fail to track because of large displacement between frames (Sec. 5)","Online camera-pose estimation, ATE RMSE [cm]; Baseline numbers taken from Point-SLAM; SplaTAM averaged over 3 seeds",{"slug":508,"group":509,"sourceId":510,"sourceLabel":511,"table":512,"selfRows":439,"metrics":513,"seqs":523,"entrants":539,"cells":557,"outcomes":815,"locators":817,"hardware":818,"wordings":819,"notes":820},"livgs2025-table-ii","livgs2025:Table II","livgs2025","Xiao et al., 2025","Table II",[514,517,520],{"label":515,"unit":516,"statistic":139,"alignment":140},"t_rel (average translational RMSE drift)","%",{"label":518,"unit":519,"statistic":139,"alignment":140},"r_rel (average rotational RMSE drift)","deg\u002F100 m",{"label":521,"unit":522,"statistic":139,"alignment":140},"t_abs (ATE RMSE)","m",[524,528,530,532,534,536],{"dataset":525,"sequence":526,"environment":527},"NTU4DRadLM","cp","outdoor, low-speed segment (cp about 230 m; garden and nyl segments at least 220 m each), Livox Horizon",{"dataset":525,"sequence":529,"environment":527},"garden1",{"dataset":525,"sequence":531,"environment":527},"garden2",{"dataset":525,"sequence":533,"environment":527},"nyl1",{"dataset":525,"sequence":535,"environment":527},"nyl2",{"dataset":525,"sequence":537,"environment":538},"loop2","outdoor, human-driven vehicle, high speed, 300 frames over about 250 m, Livox Horizon",[540,543,546,547,548,551,553,555],{"name":541,"methodId":542,"linkable":199,"proposed":71,"self":71},"NeRF-LOAM","nerfloam2023",{"name":544,"methodId":545,"linkable":71,"proposed":71,"self":71},"HDL-graph-SLAM","koide2019_hdlgraphslam",{"name":201,"methodId":202,"linkable":199,"proposed":71,"self":71},{"name":7,"methodId":5,"linkable":199,"proposed":71,"self":199},{"name":549,"methodId":550,"linkable":199,"proposed":71,"self":71},"MonoGS","monogs2024",{"name":552,"methodId":80,"linkable":71,"proposed":71,"self":71},"Gaussian-SLAM",{"name":554,"methodId":80,"linkable":71,"proposed":71,"self":71},"GS-ICP-SLAM",{"name":556,"methodId":510,"linkable":199,"proposed":199,"self":71},"Ours",[558,560,562,564,566,568,569,571,573,575,577,579,581,583,585,587,589,591,593,595,597,599,601,603,605,607,608,610,612,614,616,618,620,622,624,626,628,630,632,634,636,638,640,642,644,646,648,650,652,654,656,658,660,662,664,665,666,668,669,670,672,673,674,676,677,678,680,681,682,684,685,686,688,690,692,694,696,698,700,702,704,706,708,710,712,713,715,717,719,721,723,725,727,729,730,731,732,733,734,735,736,737,738,739,740,741,743,745,747,749,751,753,754,756,758,760,762,764,766,768,770,772,773,775,777,779,781,783,785,787,789,791,793,795,797,799,800,802,803,805,807,809,811,813],[226,226,226,559,228,226,228,228,226],2.943,[226,230,226,561,228,226,228,228,226],9.644,[226,233,226,563,228,226,228,228,226],5.39,[226,226,230,565,228,226,228,228,226],1.182,[226,230,230,567,228,226,228,228,226],0.559,[226,233,230,335,228,226,228,228,226],[226,226,233,570,228,226,228,228,226],1.213,[226,230,233,572,228,226,228,228,226],0.707,[226,233,233,574,228,226,228,228,226],1.076,[226,226,248,576,228,226,228,228,226],1.371,[226,230,248,578,228,226,228,228,226],1.14,[226,233,248,580,228,226,228,228,226],3.504,[226,226,251,582,228,226,228,228,226],1.343,[226,230,251,584,228,226,228,228,226],1.73,[226,233,251,586,228,226,228,228,226],17.46,[226,226,254,588,228,226,228,228,226],1.442,[226,230,254,590,228,226,228,228,226],2.205,[226,233,254,592,228,226,228,228,226],1.785,[230,226,226,594,228,226,228,228,226],1.264,[230,230,226,596,228,226,228,228,226],1.553,[230,233,226,598,228,226,228,228,226],1.079,[230,226,230,600,228,226,228,228,226],1.874,[230,230,230,602,228,226,228,228,226],1.603,[230,233,230,604,228,226,228,228,226],1.478,[230,226,233,606,228,226,228,228,226],1.186,[230,230,233,300,228,226,228,228,226],[230,233,233,609,228,226,228,228,226],3.154,[230,226,248,611,228,226,228,228,226],1.737,[230,230,248,613,228,226,228,228,226],1.271,[230,233,248,615,228,226,228,228,226],2.266,[230,226,251,617,228,226,228,228,226],1.514,[230,230,251,619,228,226,228,228,226],1.835,[230,233,251,621,228,226,228,228,226],17.638,[230,226,254,623,228,226,228,228,226],1.436,[230,230,254,625,228,226,228,228,226],2.802,[230,233,254,627,228,226,228,228,226],0.593,[233,226,226,629,228,226,228,228,226],1.356,[233,230,226,631,228,226,228,228,226],1.992,[233,233,226,633,228,226,228,228,226],2.865,[233,226,230,635,228,226,228,228,226],1.173,[233,230,230,637,228,226,228,228,226],0.626,[233,233,230,639,228,226,228,228,226],0.529,[233,226,233,641,228,226,228,228,226],1.212,[233,230,233,643,228,226,228,228,226],0.772,[233,233,233,645,228,226,228,228,226],1.001,[233,226,248,647,228,226,228,228,226],1.342,[233,230,248,649,228,226,228,228,226],1.172,[233,233,248,651,228,226,228,228,226],19.528,[233,226,251,653,228,226,228,228,226],1.333,[233,230,251,655,228,226,228,228,226],1.736,[233,233,251,657,228,226,228,228,226],23.283,[233,226,254,659,228,226,228,228,226],1.403,[233,230,254,661,228,226,228,228,226],2.256,[233,233,254,663,228,226,228,228,226],0.952,[248,226,226,80,226,226,228,228,226],[248,230,226,80,226,226,228,228,226],[248,233,226,667,228,226,228,228,226],2.336,[248,226,230,80,226,226,228,228,226],[248,230,230,80,226,226,228,228,226],[248,233,230,671,228,226,228,228,226],0.979,[248,226,233,80,226,226,228,228,226],[248,230,233,80,226,226,228,228,226],[248,233,233,675,228,226,228,228,226],1.221,[248,226,248,80,226,226,228,228,226],[248,230,248,80,226,226,228,228,226],[248,233,248,679,228,226,228,228,226],12.332,[248,226,251,80,226,226,228,228,226],[248,230,251,80,226,226,228,228,226],[248,233,251,683,228,226,228,228,226],17.442,[248,226,254,80,226,226,228,228,226],[248,230,254,80,226,226,228,228,226],[248,233,254,687,228,226,228,228,226],2.692,[251,226,226,689,228,226,228,228,226],4.171,[251,230,226,691,228,226,228,228,226],3.472,[251,233,226,693,228,226,228,228,226],3.44,[251,226,230,695,228,226,228,228,226],1.179,[251,230,230,697,228,226,228,228,226],0.754,[251,233,230,699,228,226,228,228,226],0.664,[251,226,233,701,228,226,228,228,226],1.163,[251,230,233,703,228,226,228,228,226],0.765,[251,233,233,705,228,226,228,228,226],0.708,[251,226,248,707,228,226,228,228,226],1.382,[251,230,248,709,228,226,228,228,226],1.175,[251,233,248,711,228,226,228,228,226],9.595,[251,226,251,576,228,226,228,228,226],[251,230,251,714,228,226,228,228,226],1.701,[251,233,251,716,228,226,228,228,226],28.553,[251,226,254,718,228,226,228,228,226],7.375,[251,230,254,720,228,226,228,228,226],5.708,[251,233,254,722,228,226,228,228,226],15.357,[254,226,226,724,228,226,228,228,226],1.249,[254,230,226,726,228,226,228,228,226],3.047,[254,233,226,728,228,226,228,228,226],1.04,[254,226,230,80,226,226,228,228,226],[254,230,230,80,226,226,228,228,226],[254,233,230,80,226,226,228,228,226],[254,226,233,80,226,226,228,228,226],[254,230,233,80,226,226,228,228,226],[254,233,233,80,226,226,228,228,226],[254,226,248,80,226,226,228,228,226],[254,230,248,80,226,226,228,228,226],[254,233,248,80,226,226,228,228,226],[254,226,251,80,226,226,228,228,226],[254,230,251,80,226,226,228,228,226],[254,233,251,80,226,226,228,228,226],[254,226,254,742,228,226,228,228,226],1.399,[254,230,254,744,228,226,228,228,226],2.384,[254,233,254,746,228,226,228,228,226],1.136,[256,226,226,748,228,226,228,228,226],5.471,[256,230,226,750,228,226,228,228,226],4.041,[256,233,226,752,228,226,228,228,226],6.33,[256,226,230,724,228,226,228,228,226],[256,230,230,755,228,226,228,228,226],0.764,[256,233,230,757,228,226,228,228,226],2.082,[256,226,233,759,228,226,228,228,226],1.824,[256,230,233,761,228,226,228,228,226],1.316,[256,233,233,763,228,226,228,228,226],5.507,[256,226,248,765,228,226,228,228,226],1.662,[256,230,248,767,228,226,228,228,226],1.771,[256,233,248,769,228,226,228,228,226],23.331,[256,226,251,771,228,226,228,228,226],2.101,[256,230,251,298,228,226,228,228,226],[256,233,251,774,228,226,228,228,226],23.915,[256,226,254,776,228,226,228,228,226],3.236,[256,230,254,778,228,226,228,228,226],2.644,[256,233,254,780,228,226,228,228,226],13.819,[259,226,226,782,228,226,228,228,226],0.234,[259,230,226,784,228,226,228,228,226],1.216,[259,233,226,786,228,226,228,228,226],0.464,[259,226,230,788,228,226,228,228,226],1.183,[259,230,230,790,228,226,228,228,226],0.716,[259,233,230,792,228,226,228,228,226],0.366,[259,226,233,794,228,226,228,228,226],1.236,[259,230,233,796,228,226,228,228,226],0.962,[259,233,233,798,228,226,228,228,226],0.679,[259,226,248,435,228,226,228,228,226],[259,230,248,801,228,226,228,228,226],1.307,[259,233,248,320,228,226,228,228,226],[259,226,251,804,228,226,228,228,226],1.106,[259,230,251,806,228,226,228,228,226],1.369,[259,233,251,808,228,226,228,228,226],0.771,[259,226,254,810,228,226,228,228,226],1.393,[259,230,254,812,228,226,228,228,226],2.239,[259,233,254,814,228,226,228,228,226],0.843,[816],"not reported ('-' in table)",[512],[],[],[821],"Tracking accuracy with rpg trajectory evaluation: t_rel = average translational RMSE drift (%), r_rel = average rotational RMSE drift (deg\u002F100 m), t_abs = ATE RMSE (m); reference trajectories from R3LIVE (not an independent measurement); alignment not stated; IMU not used by LiV-GS; '-' entries reported without explanation (text says indoor-oriented 3DGS SLAM methods degrade or fail on some outdoor sequences)",{"slug":823,"group":824,"sourceId":825,"sourceLabel":826,"table":827,"selfRows":355,"metrics":828,"seqs":838,"entrants":866,"cells":877,"outcomes":996,"locators":997,"hardware":998,"wordings":999,"notes":1000},"gaussianlic2025-table-i","gaussianlic2025:Table I","gaussianlic2025","Lang et al., 2025","Table I",[829,832,836],{"label":830,"unit":831,"statistic":140,"alignment":38},"PSNR (dB)","dB",{"label":833,"unit":834,"statistic":835,"alignment":38},"SSIM, Avg. column","unitless","mean",{"label":837,"unit":834,"statistic":835,"alignment":38},"LPIPS, Avg. column",[839,843,845,847,850,852,854,857,859,861,864],{"dataset":840,"sequence":841,"environment":842},"FAST-LIVO","f0 hku2","real-world indoor and outdoor sequences (FAST-LIVO, R3LIVE, MCD)",{"dataset":840,"sequence":844,"environment":842},"f1 LiDAR Degenerate",{"dataset":840,"sequence":846,"environment":842},"f2 Visual Challenge",{"dataset":848,"sequence":849,"environment":842},"R3LIVE","r0 hku_campus_seq_00",{"dataset":848,"sequence":851,"environment":842},"r1 degenerate_seq_00",{"dataset":848,"sequence":853,"environment":842},"r2 degenerate_seq_01",{"dataset":855,"sequence":856,"environment":842},"MCD","m0 tuhh_day_02 segment",{"dataset":855,"sequence":858,"environment":842},"m1 tuhh_day_03 segment",{"dataset":855,"sequence":860,"environment":842},"m2 tuhh_day_04 segment",{"dataset":862,"sequence":863,"environment":842},"FAST-LIVO, R3LIVE and MCD","Avg. average",{"dataset":862,"sequence":865,"environment":842},"average of 9 sequences",[867,869,871,873,875],{"name":868,"methodId":80,"linkable":71,"proposed":71,"self":71},"NeRF-SLAM (train view)",{"name":870,"methodId":550,"linkable":199,"proposed":71,"self":71},"MonoGS (train view)",{"name":872,"methodId":5,"linkable":199,"proposed":71,"self":199},"SplaTAM with LiDAR pseudo RGB-D (train view)",{"name":874,"methodId":825,"linkable":199,"proposed":199,"self":71},"Gaussian-LIC (train view)",{"name":876,"methodId":825,"linkable":199,"proposed":199,"self":71},"Gaussian-LIC (novel view)",[878,880,882,884,885,887,889,891,893,895,897,899,901,903,905,907,909,911,913,915,917,919,921,923,925,927,929,931,933,935,937,939,941,943,945,947,949,951,953,955,957,959,961,963,965,967,969,971,973,975,977,979,981,983,984,986,988,990,992,994],[226,226,226,879,228,226,228,228,226],25.56,[226,226,230,881,228,226,228,228,226],25.47,[226,226,233,883,228,226,228,228,226],17.01,[226,226,248,410,228,226,228,228,226],[226,226,251,886,228,226,228,228,226],21.2,[226,226,254,888,228,226,228,228,226],15.02,[226,226,256,890,228,226,228,228,226],18.7,[226,226,259,892,228,226,228,228,226],18.93,[226,226,262,894,228,226,228,228,226],15.4,[226,226,265,896,228,226,228,228,226],19.65,[230,226,226,898,228,226,228,228,226],23.58,[230,226,230,900,228,226,228,228,226],23.45,[230,226,233,902,228,226,228,228,226],17.04,[230,226,248,904,228,226,228,228,226],16.19,[230,226,251,906,228,226,228,228,226],15.03,[230,226,254,908,228,226,228,228,226],15.09,[230,226,256,910,228,226,228,228,226],16.41,[230,226,259,912,228,226,228,228,226],13.69,[230,226,262,914,228,226,228,228,226],14.91,[230,226,265,916,228,226,228,228,226],17.27,[233,226,226,918,228,226,228,228,226],25.51,[233,226,230,920,228,226,228,228,226],27.4,[233,226,233,922,228,226,228,228,226],17.84,[233,226,248,924,228,226,228,228,226],17.1,[233,226,251,926,228,226,228,228,226],19.24,[233,226,254,928,228,226,228,228,226],18.3,[233,226,256,930,228,226,228,228,226],13.68,[233,226,259,932,228,226,228,228,226],13.17,[233,226,262,934,228,226,228,228,226],10.01,[233,226,265,936,228,226,228,228,226],18.03,[248,226,226,938,228,226,228,228,226],29.89,[248,226,230,940,228,226,228,228,226],31.28,[248,226,233,942,228,226,228,228,226],23.9,[248,226,248,944,228,226,228,228,226],25.27,[248,226,251,946,228,226,228,228,226],22.47,[248,226,254,948,228,226,228,228,226],23.49,[248,226,256,950,228,226,228,228,226],21.06,[248,226,259,952,228,226,228,228,226],22.87,[248,226,262,954,228,226,228,228,226],20.73,[248,226,265,956,228,226,228,228,226],24.55,[251,226,226,958,228,226,228,228,226],29.28,[251,226,230,960,228,226,228,228,226],30.91,[251,226,233,962,228,226,228,228,226],23.28,[251,226,248,964,228,226,228,228,226],24.52,[251,226,251,966,228,226,228,228,226],22.03,[251,226,254,968,228,226,228,228,226],22.59,[251,226,256,970,228,226,228,228,226],20.19,[251,226,259,972,228,226,228,228,226],22.12,[251,226,262,974,228,226,228,228,226],19.57,[251,226,265,976,228,226,228,228,226],23.83,[226,230,268,978,228,226,228,228,226],0.603,[226,233,268,980,228,226,228,228,226],0.405,[230,230,268,982,228,226,228,228,226],0.548,[230,233,268,310,228,226,228,228,226],[233,230,268,985,228,226,228,228,226],0.592,[233,233,268,987,228,226,228,228,226],0.361,[248,230,268,989,228,226,228,228,226],0.77,[248,233,268,991,228,226,228,228,226],0.231,[251,230,268,993,228,226,228,228,226],0.738,[251,233,268,995,228,226,228,228,226],0.236,[],[827],[],[],[1001],"Rendering quality; compared methods mapped with ground-truth poses (MCD) or Gaussian-LIC estimated poses (FAST-LIVO, R3LIVE); FAST-LIVO and R3LIVE use a solid-state LiDAR, MCD a spinning LiDAR",{"slug":1003,"group":1004,"sourceId":1005,"sourceLabel":1006,"table":133,"selfRows":265,"metrics":1007,"seqs":1009,"entrants":1029,"cells":1047,"outcomes":1139,"locators":1140,"hardware":1142,"wordings":1143,"notes":1144},"gsicpslam2024-table-1","gsicpslam2024:Table 1","gsicpslam2024","Ha et al., 2024",[1008],{"label":137,"unit":138,"statistic":139,"alignment":140},[1010,1013,1015,1017,1019,1021,1023,1025,1027],{"dataset":150,"sequence":1011,"environment":1012},"R0","synthetic indoor scenes",{"dataset":150,"sequence":1014,"environment":1012},"R1",{"dataset":150,"sequence":1016,"environment":1012},"R2",{"dataset":150,"sequence":1018,"environment":1012},"Of0",{"dataset":150,"sequence":1020,"environment":1012},"Of1",{"dataset":150,"sequence":1022,"environment":1012},"Of2",{"dataset":150,"sequence":1024,"environment":1012},"Of3",{"dataset":150,"sequence":1026,"environment":1012},"Of4",{"dataset":150,"sequence":1028,"environment":1012},"average of 8 scenes",[1030,1032,1034,1036,1039,1041,1043,1045],{"name":1031,"methodId":211,"linkable":199,"proposed":71,"self":71},"NICE-SLAM* [ 47 ]",{"name":1033,"methodId":198,"linkable":199,"proposed":71,"self":71},"Point-SLAM* [ 32 ]",{"name":1035,"methodId":80,"linkable":71,"proposed":71,"self":71},"GS-SLAM [ 44 ]",{"name":1037,"methodId":1038,"linkable":199,"proposed":71,"self":71},"Photo-SLAM [ 12 ]","photoslam2024",{"name":1040,"methodId":5,"linkable":199,"proposed":71,"self":199},"SplaTAM* [ 14 ]",{"name":1042,"methodId":80,"linkable":71,"proposed":199,"self":71},"Ours (limited to 30 FPS)",{"name":1044,"methodId":550,"linkable":199,"proposed":71,"self":71},"Gaussian Splatting SLAM* [22] (ECCV version only)",{"name":1046,"methodId":202,"linkable":199,"proposed":71,"self":71},"ORB-SLAM3 [4] (ECCV version only)",[1048,1050,1052,1053,1055,1057,1059,1061,1063,1065,1067,1069,1070,1071,1073,1074,1075,1077,1078,1079,1080,1081,1082,1083,1084,1085,1086,1088,1089,1090,1091,1092,1093,1094,1095,1096,1097,1098,1099,1101,1103,1105,1106,1108,1109,1110,1112,1114,1116,1118,1120,1122,1123,1124,1125,1126,1128,1129,1130,1132,1134,1135,1136,1137],[226,226,226,1049,228,226,228,228,226],1.61,[226,226,230,1051,228,226,228,228,226],1.48,[226,226,233,1049,228,226,228,228,226],[226,226,248,1054,228,226,228,228,226],0.95,[226,226,251,1056,228,226,228,228,226],0.81,[226,226,254,1058,228,226,228,228,226],1.46,[226,226,256,1060,228,226,228,228,226],1.76,[226,226,259,1062,228,226,228,228,226],1.69,[226,226,262,1064,228,226,228,228,226],1.42,[230,226,226,1066,228,226,228,228,226],0.59,[230,226,230,1068,228,226,228,228,226],0.51,[230,226,233,352,228,226,228,228,226],[230,226,248,257,228,226,228,228,226],[230,226,251,1072,228,226,228,228,226],0.46,[230,226,254,333,228,226,228,228,226],[230,226,256,326,228,226,228,228,226],[230,226,259,1076,228,226,228,228,226],0.87,[230,226,262,335,228,226,228,228,226],[233,226,226,333,228,226,228,228,226],[233,226,230,252,228,226,228,228,226],[233,226,233,269,228,226,228,228,226],[233,226,248,314,228,226,228,228,226],[233,226,251,328,228,226,228,228,226],[233,226,254,1066,228,226,228,228,226],[233,226,256,1072,228,226,228,228,226],[233,226,259,312,228,226,228,228,226],[233,226,262,1087,228,226,228,228,226],0.5,[248,226,226,80,226,226,228,228,226],[248,226,230,80,226,226,228,228,226],[248,226,233,80,226,226,228,228,226],[248,226,248,80,226,226,228,228,226],[248,226,251,80,226,226,228,228,226],[248,226,254,80,226,226,228,228,226],[248,226,256,80,226,226,228,228,226],[248,226,259,80,226,226,228,228,226],[248,226,262,244,228,226,228,228,226],[251,226,226,345,228,226,228,228,226],[251,226,230,260,228,226,228,228,226],[251,226,233,1100,228,226,228,228,226],0.28,[251,226,248,1102,228,226,228,228,226],0.49,[251,226,251,1104,228,226,228,228,226],0.21,[251,226,254,341,228,226,228,228,226],[251,226,256,1107,228,226,228,228,226],0.34,[251,226,259,316,228,226,228,228,226],[251,226,262,266,228,226,228,228,226],[254,226,226,1111,228,226,228,228,226],0.15,[254,226,230,1113,228,226,228,228,226],0.16,[254,226,233,1115,228,226,228,228,226],0.11,[254,226,248,1117,228,226,228,228,226],0.18,[254,226,251,1119,228,226,228,228,226],0.12,[254,226,254,1121,228,226,228,228,226],0.17,[254,226,256,1113,228,226,228,228,226],[254,226,259,1104,228,226,228,228,226],[254,226,262,1113,228,226,228,228,226],[256,226,226,269,228,230,228,228,230],[256,226,230,1127,228,230,228,228,230],0.22,[256,226,233,345,228,230,228,228,230],[256,226,248,266,228,230,228,228,230],[256,226,251,1131,228,230,228,228,230],0.19,[256,226,254,1133,228,230,228,228,230],0.25,[256,226,256,1119,228,230,228,228,230],[256,226,259,1056,228,230,228,228,230],[256,226,262,352,228,230,228,228,230],[259,226,262,1138,228,230,228,228,233],1.8,[140],[133,1141],"Table 1 (ECCV 2024 version of record)",[],[],[1145,1146,1147],"Replica ATE RMSE; * = reproduced with official code; GS-SLAM from its paper, Photo-SLAM only average from its paper","Replica ATE RMSE; * = reproduced with official code; GS-SLAM from its paper, Photo-SLAM only average from its paper; the Gaussian Splatting SLAM (MonoGS) row appears only in the ECCV version of record, reproduced with official code and evaluated on keyframes only","Replica ATE RMSE; the ECCV 2024 version of record (Table 1) adds an ORB-SLAM3 average taken from Photo-SLAM [12]; per-scene cells are '-'",[1149,1158,1164,1169,1175,1180,1185,1192,1196,1202,1206,1211,1216,1220,1224,1230,1234,1240,1245,1249,1254,1261,1265,1270,1275],{"group":1150,"slug":1151,"sourceLabel":1152,"table":1153,"selfRows":265,"datasets":1154},"gslivo2025:Table III","gslivo2025-table-iii","Hong et al., 2025","Table III",[1155,1156,1157],"MARS-LVIG","Oxford Spires","proprietary 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6",[150],{"group":1186,"slug":1187,"sourceLabel":1188,"table":1189,"selfRows":254,"datasets":1190},"deng2026_mcgs_slam:Table 11","deng2026-mcgs-slam-table-11","Deng & Gan, 2026","Table 11",[1191],"EuRoC MAV",{"group":1193,"slug":1194,"sourceLabel":826,"table":512,"selfRows":251,"datasets":1195},"gaussianlic2025:Table II","gaussianlic2025-table-ii",[840],{"group":1197,"slug":1198,"sourceLabel":1006,"table":1199,"selfRows":251,"datasets":1200},"gsicpslam2024:Table 2","gsicpslam2024-table-2","Table 2",[1201],"TUM RGB-D",{"group":1203,"slug":1204,"sourceLabel":1006,"table":1178,"selfRows":251,"datasets":1205},"gsicpslam2024:Table 3","gsicpslam2024-table-3",[150],{"group":1207,"slug":1208,"sourceLabel":1006,"table":1209,"selfRows":251,"datasets":1210},"gsicpslam2024:Table 4","gsicpslam2024-table-4","Table 4",[1201],{"group":1212,"slug":1213,"sourceLabel":1172,"table":1214,"selfRows":251,"datasets":1215},"rtgslam2024:Supp. Table 5","rtgslam2024-supp-table-5","Supp. Table 5",[150],{"group":1217,"slug":1218,"sourceLabel":1172,"table":1199,"selfRows":251,"datasets":1219},"rtgslam2024:Table 2","rtgslam2024-table-2",[1201],{"group":1221,"slug":1222,"sourceLabel":1172,"table":1178,"selfRows":251,"datasets":1223},"rtgslam2024:Table 3","rtgslam2024-table-3",[87],{"group":1225,"slug":1226,"sourceLabel":1227,"table":1178,"selfRows":251,"datasets":1228},"yuan2026_adaptive3dgsslam:Table 3","yuan2026-adaptive3dgsslam-table-3","Yuan et al., 2026",[1229],"ReplicaCAD (FRL apartment)",{"group":1231,"slug":1232,"sourceLabel":1188,"table":133,"selfRows":248,"datasets":1233},"deng2026_mcgs_slam:Table 1","deng2026-mcgs-slam-table-1",[1201],{"group":1235,"slug":1236,"sourceLabel":511,"table":1237,"selfRows":248,"datasets":1238},"livgs2025:Table IV","livgs2025-table-iv","Table IV",[1239],"R3LIVE dataset",{"group":1241,"slug":1242,"sourceLabel":1161,"table":1243,"selfRows":248,"datasets":1244},"yan2026_underground3dgsslam:Table VI","yan2026-underground3dgsslam-table-vi","Table VI",[1201],{"group":1246,"slug":1247,"sourceLabel":1227,"table":1209,"selfRows":248,"datasets":1248},"yuan2026_adaptive3dgsslam:Table 4","yuan2026-adaptive3dgsslam-table-4",[1229],{"group":1250,"slug":1251,"sourceLabel":1172,"table":1252,"selfRows":233,"datasets":1253},"rtgslam2024:Supp. Table 7","rtgslam2024-supp-table-7","Supp. Table 7",[1201],{"group":1255,"slug":1256,"sourceLabel":1257,"table":1258,"selfRows":233,"datasets":1259},"tosi2026survey:Table XI","tosi2026survey-table-xi","Tosi et al., 2026","Table XI",[1260,150],"KITTI",{"group":1262,"slug":1263,"sourceLabel":1188,"table":1199,"selfRows":230,"datasets":1264},"deng2026_mcgs_slam:Table 2","deng2026-mcgs-slam-table-2",[1201],{"group":1266,"slug":1267,"sourceLabel":1188,"table":1268,"selfRows":230,"datasets":1269},"deng2026_mcgs_slam:Table 9","deng2026-mcgs-slam-table-9","Table 9",[1201],{"group":1271,"slug":1272,"sourceLabel":1273,"table":827,"selfRows":230,"datasets":1274},"hislam2_2025:Table I","hislam2-2025-table-i","Zhang et al., 2025",[150],{"group":1276,"slug":1277,"sourceLabel":1172,"table":1278,"selfRows":230,"datasets":1279},"rtgslam2024:Supp. Table 6","rtgslam2024-supp-table-6","Supp. Table 6",[150],1790510665896]