[{"data":1,"prerenderedAt":1196},["ShallowReactive",2],{"method-droidslam2021":3},{"method":4,"reference":60,"equipment":79,"figures":112,"results":113},{"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":29,"sensors":35,"platform":39,"estimator":40,"association":41,"timeModel":42,"deskew":43,"loopClosure":44,"globalOptimization":45,"mapRepresentation":46,"prior":47,"outputGeometry":48,"compute":49,"codeUrl":50,"codeLicense":51,"relatedVersions":52},"droidslam2021","Teed & Deng, 2021","DROID-SLAM","DROID-SLAM: Deep Visual SLAM for Monocular, Stereo, and RGB-D Cameras",2021,"recent","C09","full_slam_with_global_correction","DROID-SLAM 以卷積 GRU 反覆預測稠密光流修正，並透過可微分稠密光束調整（dense bundle adjustment, DBA）同步更新相機位姿與逐像素反深度。前端做局部光束調整，後端對全部關鍵影格做全域光束調整，回訪時加入長距離邊以形成迴圈。網路僅以合成 TartanAir 單目影片訓練，可在測試時使用雙目或 RGB-D。作者明言 SLAM 重建缺乏標準幾何評估協定，因此未評估稠密點雲精度。","Learned dense-flow updates coupled with a differentiable dense BA layer jointly refine poses and per-pixel depth, with frontend local BA and backend global BA.","full_text_reviewed","peer_reviewed_published","main_body","論文未在營建或基礎設施場域測試；僅公開資料集（TartanAir、EuRoC、TUM-RGBD、ETH3D）。",[20,21],"simulation","public_benchmark",[23,24,25,26,27,28],"Monocular EuRoC average ATE 2.2 cm with no failures (Sec. 4, EuRoC)","Tracks 30 of 32 ETH3D RGB-D sequences (Sec. 1)","Generalizes across datasets despite synthetic-only training (Sec. 4)","Stereo EuRoC average ATE 0.024 m, 71% lower than ORB-SLAM3 (0.084 m) (App. A, Table 5)","Tracks all 9 TUM-RGBD freiburg1 sequences from monocular video, average 0.038 (Table 4)","ETH3D-SLAM AUC 340.42 (train) and 207.79 (test), first on both splits (Fig. 4)",[30,31,32,33,34],"Backend memory intensive; long sequences need a 24 GB GPU (Sec. 4 timing and memory)","No dense 3D reconstruction evaluation; authors state no standard protocol exists (Sec. 4)","Follow-up [mast3rslam2025] reports DROID-SLAM per-pixel depth BA permits incoherent geometry and produces many noisy points on EuRoC (MASt3R-SLAM Sec. 4.2 and Sec. 5)","Reported real-time rates rely on downsampling and skipping every other frame; TartanAir runs at about 8 fps (Sec. 4)","ATE alignment and statistic are not stated in any table (Tables 1 to 5)",[36,37,38],"monocular camera","stereo","RGB-D",[],"recurrent learned update operator + differentiable dense bundle adjustment (Gauss-Newton, block-sparse Cholesky) over a keyframe frame-graph","learned dense optical flow via correlation volumes","discrete poses","not_applicable","long-range co-visibility edges added to the frame graph when revisiting mapped regions","backend global dense bundle adjustment over the full keyframe history","per-keyframe dense inverse-depth maps","network trained on synthetic TartanAir monocular video; no scene prior at test time","camera trajectory and per-pixel inverse depth of keyframes (dense points by back-projection); authors did not evaluate 3D reconstructions","real time with two 3090 GPUs (tracking and local BA on one, global BA and loop closure on the other); EuRoC about 20 fps at 320x512 with every other frame skipped; TUM-RGBD about 30 fps at 240x320 with every other frame skipped; TartanAir about 8 fps (not real time); frontend fits an 8 GB GPU, all TUM results on one 1080Ti, EuRoC, TartanAir and ETH3D need 24 GB; training 1 week on 4 RTX-3090","https:\u002F\u002Fgithub.com\u002Fprinceton-vl\u002FDROID-SLAM","BSD-3-Clause",[53,57],{"relation":54,"title":55,"doi_or_url":56},"preprint","arXiv:2108.10869","https:\u002F\u002Farxiv.org\u002Fabs\u002F2108.10869",{"relation":58,"title":59,"doi_or_url":50},"code_release","princeton-vl\u002FDROID-SLAM",{"id":5,"kind":61,"shortName":7,"title":8,"authors":62,"year":9,"venue":65,"venueType":66,"publisher":67,"volumeIssuePages":68,"doi":69,"arxivId":70,"url":71,"firstPublicDate":72,"publicationStatus":16,"metadataStatus":73,"fulltextStatus":15,"era":10,"classicReason":43,"codeUrl":50,"cluster":11,"topics":74,"mdpi":75,"verification":76,"label":6,"fulltextRoute":77,"versionRead":78,"addedByCensus":75},"method",[63,64],"Zachary Teed","Jia Deng","Advances in Neural Information Processing Systems 34 (NeurIPS 2021)","conference","NeurIPS Foundation (Curran Associates proceedings)","vol. 34, pp. 16558-16569",null,"2108.10869","https:\u002F\u002Fproceedings.neurips.cc\u002Fpaper_files\u002Fpaper\u002F2021\u002Fhash\u002F89fcd07f20b6785b92134bd6c1d0fa42-Abstract.html","2021-08-24","metadata_verified",[11],false,"confirmed","arXiv","arXiv v2 (2108.10869v2, 2 Feb 2022) including appendices A to D; NeurIPS 2021 proceedings version not compared",[80,86,89,93,100,106],{"category":81,"model":82,"canonical":82,"role":83,"dataset":69,"specs":84,"locator":85},"compute","3090 GPU (x2)","compute for runtime","real-time configuration: tracking and local BA on the first GPU, global BA and loop closure on the second","Sec. 4 (Timing and Memory)",{"category":81,"model":87,"canonical":87,"role":83,"dataset":69,"specs":88,"locator":85},"1080Ti graphics card","sufficient for all TUM-RGBD results",{"category":81,"model":90,"canonical":90,"role":83,"dataset":69,"specs":91,"locator":92},"RTX-3090 GPU (x4)","training compute, not runtime: 250k steps, batch 4, 1 week","Sec. 4",{"category":94,"model":95,"canonical":95,"role":96,"dataset":97,"specs":98,"locator":99},"platform","micro aerial vehicle (MAV)","dataset sensor","EuRoC MAV","EuRoC video captured from sensors on board","Sec. 4 (EuRoC)",{"category":101,"model":102,"canonical":102,"role":96,"dataset":103,"specs":104,"locator":105},"camera","handheld camera","TUM RGB-D","TUM-RGBD indoor scenes with rolling shutter artifacts, motion blur and heavy rotation","Sec. 4 (TUM-RGBD)",{"category":107,"model":108,"canonical":108,"role":96,"dataset":109,"specs":110,"locator":111},"rgbd","RGB-D camera","ETH3D SLAM","ETH3D-SLAM benchmark input; 'dark' datasets without image data skipped","Sec. 4 (ETH3D-SLAM)",[],{"totalRows":114,"groupCount":115,"groups":116,"others":1021},277,40,[117,352,621,867],{"slug":118,"group":119,"sourceId":120,"sourceLabel":121,"table":122,"selfRows":123,"metrics":124,"seqs":132,"entrants":153,"cells":176,"outcomes":346,"locators":347,"hardware":348,"wordings":349,"notes":350},"goslam2023-table-3","goslam2023:Table 3","goslam2023","Zhang et al., 2023b","Table 3",36,[125,130],{"label":126,"unit":127,"statistic":128,"alignment":129},"ATE [cm] (RGB-D)","cm","RMSE","not_reported",{"label":131,"unit":127,"statistic":128,"alignment":129},"ATE [cm] (Mono.)",[133,137,139,141,143,145,147,149,151],{"dataset":134,"sequence":135,"environment":136},"ScanNet","scene0000_00","real indoor rooms",{"dataset":134,"sequence":138,"environment":136},"scene0054_00",{"dataset":134,"sequence":140,"environment":136},"scene0233_00",{"dataset":134,"sequence":142,"environment":136},"scene0465_00",{"dataset":134,"sequence":144,"environment":136},"scene0059_00",{"dataset":134,"sequence":146,"environment":136},"scene0106_00",{"dataset":134,"sequence":148,"environment":136},"scene0169_00",{"dataset":134,"sequence":150,"environment":136},"scene0181_00",{"dataset":134,"sequence":152,"environment":136},"average of 8 scenes",[154,158,161,163,165,167,170,172,174],{"name":155,"methodId":156,"linkable":157,"proposed":75,"self":75},"iMAP ∗ [ 35 ] (RGB-D)","imap2021",true,{"name":159,"methodId":160,"linkable":157,"proposed":75,"self":75},"NICE-SLAM [ 53 ] (RGB-D)","niceslam2022",{"name":162,"methodId":5,"linkable":157,"proposed":75,"self":157},"DROID-SLAM [ 41 ] (VO) (RGB-D)",{"name":164,"methodId":5,"linkable":157,"proposed":75,"self":157},"DROID-SLAM [ 41 ] (RGB-D)",{"name":166,"methodId":69,"linkable":75,"proposed":157,"self":75},"Ours (RGB-D)",{"name":168,"methodId":169,"linkable":157,"proposed":75,"self":75},"ORB-SLAM3 [ 6 ] (monocular)","orbslam3_2021",{"name":171,"methodId":5,"linkable":157,"proposed":75,"self":157},"DROID-SLAM [ 41 ] (VO) (monocular)",{"name":173,"methodId":5,"linkable":157,"proposed":75,"self":157},"DROID-SLAM [ 41 ] (monocular)",{"name":175,"methodId":69,"linkable":75,"proposed":157,"self":75},"Ours (monocular)",[177,181,184,187,190,193,196,199,202,205,207,209,211,213,215,217,219,221,223,224,226,228,230,232,234,235,237,239,241,243,245,247,249,251,253,255,257,259,261,263,265,267,269,271,273,275,277,279,281,283,285,287,289,291,293,295,297,299,301,303,305,307,309,311,313,315,317,319,320,322,324,326,328,330,332,334,336,338,340,342,344],[178,178,178,179,180,178,180,180,178],0,55.95,-1,[178,178,182,183,180,178,180,180,178],1,70.11,[178,178,185,186,180,178,180,180,178],2,86.42,[178,178,188,189,180,178,180,180,178],3,85.03,[178,178,191,192,180,178,180,180,178],4,32.06,[178,178,194,195,180,178,180,180,178],5,17.5,[178,178,197,198,180,178,180,180,178],6,70.51,[178,178,200,201,180,178,180,180,178],7,32.1,[178,178,203,204,180,178,180,180,178],8,56.21,[182,178,178,206,180,178,180,180,178],8.64,[182,178,182,208,180,178,180,180,178],20.93,[182,178,185,210,180,178,180,180,178],9,[182,178,188,212,180,178,180,180,178],22.31,[182,178,191,214,180,178,180,180,178],12.25,[182,178,194,216,180,178,180,180,178],8.09,[182,178,197,218,180,178,180,180,178],10.28,[182,178,200,220,180,178,180,180,178],12.93,[182,178,203,222,180,178,180,180,178],13.05,[185,178,178,203,180,178,180,180,178],[185,178,182,225,180,178,180,180,178],29.28,[185,178,185,227,180,178,180,180,178],6.75,[185,178,188,229,180,178,180,180,178],11.37,[185,178,191,231,180,178,180,180,178],11.3,[185,178,194,233,180,178,180,180,178],9.97,[185,178,197,206,180,178,180,180,178],[185,178,200,236,180,178,180,180,178],7.38,[185,178,203,238,180,178,180,180,178],11.59,[188,178,178,240,180,178,180,180,178],5.36,[188,178,182,242,180,178,180,180,178],8.89,[188,178,185,244,180,178,180,180,178],4.9,[188,178,188,246,180,178,180,180,178],8.32,[188,178,191,248,180,178,180,180,178],7.72,[188,178,194,250,180,178,180,180,178],7.06,[188,178,197,252,180,178,180,180,178],8.01,[188,178,200,254,180,178,180,180,178],6.97,[188,178,203,256,180,178,180,180,178],7.15,[191,178,178,258,180,178,180,180,178],5.35,[191,178,182,260,180,178,180,180,178],8.75,[191,178,185,262,180,178,180,180,178],4.78,[191,178,188,264,180,178,180,180,178],8.15,[191,178,191,266,180,178,180,180,178],7.52,[191,178,194,268,180,178,180,180,178],7.03,[191,178,197,270,180,178,180,180,178],7.74,[191,178,200,272,180,178,180,180,178],6.84,[191,178,203,274,180,178,180,180,178],7.02,[194,182,178,276,180,178,180,180,178],73.93,[194,182,182,278,180,178,180,180,178],243.26,[194,182,185,280,180,178,180,180,178],25.01,[194,182,188,282,180,178,180,180,178],181.86,[194,182,191,284,180,178,180,180,178],90.67,[194,182,194,286,180,178,180,180,178],178.13,[194,182,197,288,180,178,180,180,178],60.15,[194,182,200,290,180,178,180,180,178],104.93,[194,182,203,292,180,178,180,180,178],119.74,[197,182,178,294,180,178,180,180,178],11.05,[197,182,182,296,180,178,180,180,178],204.31,[197,182,185,298,180,178,180,180,178],71.08,[197,182,188,300,180,178,180,180,178],117.84,[197,182,191,302,180,178,180,180,178],67.26,[197,182,194,304,180,178,180,180,178],11.2,[197,182,197,306,180,178,180,180,178],16.21,[197,182,200,308,180,178,180,180,178],9.94,[197,182,203,310,180,178,180,180,178],63.61,[200,182,178,312,180,178,180,180,178],5.48,[200,182,182,314,180,178,180,180,178],197.71,[200,182,185,316,180,178,180,180,178],72.23,[200,182,188,318,180,178,180,180,178],114.36,[200,182,191,210,180,178,180,180,178],[200,182,194,321,180,178,180,180,178],6.76,[200,182,197,323,180,178,180,180,178],7.86,[200,182,200,325,180,178,180,180,178],7.41,[200,182,203,327,180,178,180,180,178],52.6,[203,182,178,329,180,178,180,180,178],5.94,[203,182,182,331,180,178,180,180,178],13.29,[203,182,185,333,180,178,180,180,178],5.31,[203,182,188,335,180,178,180,180,178],79.51,[203,182,191,337,180,178,180,180,178],8.27,[203,182,194,339,180,178,180,180,178],8.07,[203,182,197,341,180,178,180,180,178],8.42,[203,182,200,343,180,178,180,180,178],8.29,[203,182,203,345,180,178,180,180,178],17.59,[],[122],[],[],[351],"ATE RMSE on 8 ScanNet scenes, RGB-D and monocular input; iMAP and NICE-SLAM values copied from NICE-SLAM; DROID-SLAM (VO) is DROID-SLAM without final global BA",{"slug":353,"group":354,"sourceId":355,"sourceLabel":356,"table":357,"selfRows":358,"metrics":359,"seqs":366,"entrants":395,"cells":413,"outcomes":611,"locators":613,"hardware":615,"wordings":617,"notes":618},"dpvslam2024-table-2b","dpvslam2024:Table 2b","dpvslam2024","Lipson et al., 2024","Table 2b",26,[360,363],{"label":361,"unit":362,"statistic":129,"alignment":129},"ATE[m]","m",{"label":364,"unit":364,"statistic":365,"alignment":43},"FPS","mean",[367,371,373,375,377,379,381,383,385,387,389,391,393],{"dataset":368,"sequence":369,"environment":370},"KITTI","seq 00","outdoor urban driving",{"dataset":368,"sequence":372,"environment":370},"seq 01",{"dataset":368,"sequence":374,"environment":370},"seq 02",{"dataset":368,"sequence":376,"environment":370},"seq 03",{"dataset":368,"sequence":378,"environment":370},"seq 04",{"dataset":368,"sequence":380,"environment":370},"seq 05",{"dataset":368,"sequence":382,"environment":370},"seq 06",{"dataset":368,"sequence":384,"environment":370},"seq 07",{"dataset":368,"sequence":386,"environment":370},"seq 08",{"dataset":368,"sequence":388,"environment":370},"seq 09",{"dataset":368,"sequence":390,"environment":370},"seq 10",{"dataset":368,"sequence":392,"environment":370},"average of 11 sequences",{"dataset":368,"sequence":394,"environment":370},"sequences 00-10",[396,399,401,403,405,407,409,411],{"name":397,"methodId":398,"linkable":157,"proposed":75,"self":75},"ORB-SLAM2 [ 18 ]","orbslam2_2017",{"name":400,"methodId":169,"linkable":157,"proposed":75,"self":75},"ORB-SLAM3 [ 2 ]",{"name":402,"methodId":69,"linkable":75,"proposed":75,"self":75},"LDSO [ 11 ]",{"name":404,"methodId":5,"linkable":157,"proposed":75,"self":157},"DROID-VO [ 31 ]",{"name":406,"methodId":69,"linkable":75,"proposed":75,"self":75},"DPVO [ 32 ]",{"name":408,"methodId":5,"linkable":157,"proposed":75,"self":157},"DROID-SLAM [ 31 ]",{"name":410,"methodId":69,"linkable":75,"proposed":157,"self":75},"DPV-SLAM",{"name":412,"methodId":69,"linkable":75,"proposed":157,"self":75},"DPV-SLAM++",[414,415,416,418,420,422,424,426,428,430,432,435,437,440,442,443,445,447,449,451,453,455,457,459,461,462,463,465,467,469,471,473,475,477,479,481,483,485,487,489,491,493,495,497,498,500,502,504,506,508,510,512,514,516,518,520,522,524,526,528,530,532,534,536,538,540,542,544,545,547,548,550,552,554,556,557,559,560,561,563,565,567,569,571,573,575,577,579,581,583,585,587,589,591,593,594,596,598,600,602,604,606,608,610],[178,178,178,337,180,178,180,180,178],[178,178,182,69,178,178,180,180,178],[178,178,185,417,180,178,180,180,178],26.86,[178,178,188,419,180,178,180,180,178],1.21,[178,178,191,421,180,178,180,180,178],0.77,[178,178,194,423,180,178,180,180,178],7.91,[178,178,197,425,180,178,180,180,178],12.54,[178,178,200,427,180,178,180,180,178],3.44,[178,178,203,429,180,178,180,180,178],46.81,[178,178,210,431,180,178,180,180,178],76.54,[178,178,433,434,180,178,180,180,178],10,6.61,[178,178,436,69,182,178,180,180,178],11,[178,182,438,439,180,178,178,180,182],12,34,[182,178,178,441,180,178,180,180,178],6.77,[182,178,182,69,178,178,180,180,178],[182,178,185,444,180,178,180,180,178],30.5,[182,178,188,446,180,178,180,180,178],1.036,[182,178,191,448,180,178,180,180,178],0.93,[182,178,194,450,180,178,180,180,178],5.542,[182,178,197,452,180,178,180,180,178],16.605,[182,178,200,454,180,178,180,180,178],9.7,[182,178,203,456,180,178,180,180,178],60.687,[182,178,210,458,180,178,180,180,178],7.899,[182,178,433,460,180,178,180,180,178],8.65,[182,178,436,69,182,178,180,180,178],[182,182,438,439,180,178,178,180,182],[185,178,178,464,180,178,180,180,178],9.32,[185,178,182,466,180,178,180,180,178],11.68,[185,178,185,468,180,178,180,180,178],31.98,[185,178,188,470,180,178,180,180,178],2.85,[185,178,191,472,180,178,180,180,178],1.22,[185,178,194,474,180,178,180,180,178],5.1,[185,178,197,476,180,178,180,180,178],13.55,[185,178,200,478,180,178,180,180,178],2.96,[185,178,203,480,180,178,180,180,178],129.02,[185,178,210,482,180,178,180,180,178],21.64,[185,178,433,484,180,178,180,180,178],17.36,[185,178,436,486,180,178,180,180,178],22.42,[185,182,438,488,180,178,178,180,182],49,[188,178,178,490,180,178,180,180,178],98.43,[188,178,182,492,180,178,180,180,178],84.2,[188,178,185,494,180,178,180,180,178],108.8,[188,178,188,496,180,178,180,180,178],2.58,[188,178,191,448,180,178,180,180,178],[188,178,194,499,180,178,180,180,178],59.27,[188,178,197,501,180,178,180,180,178],64.4,[188,178,200,503,180,178,180,180,178],24.2,[188,178,203,505,180,178,180,180,178],64.55,[188,178,210,507,180,178,180,180,178],71.8,[188,178,433,509,180,178,180,180,178],16.91,[188,178,436,511,180,178,180,180,178],54.19,[188,182,438,513,180,178,178,180,182],17,[191,178,178,515,180,178,180,180,178],113.21,[191,178,182,517,180,178,180,180,178],12.69,[191,178,185,519,180,178,180,180,178],123.4,[191,178,188,521,180,178,180,180,178],2.09,[191,178,191,523,180,178,180,180,178],0.68,[191,178,194,525,180,178,180,180,178],58.96,[191,178,197,527,180,178,180,180,178],54.78,[191,178,200,529,180,178,180,180,178],19.26,[191,178,203,531,180,178,180,180,178],115.9,[191,178,210,533,180,178,180,180,178],75.1,[191,178,433,535,180,178,180,180,178],13.63,[191,178,436,537,180,178,180,180,178],53.61,[191,182,438,539,180,178,178,180,182],48,[194,178,178,541,180,178,180,180,178],92.1,[194,178,182,543,180,178,180,180,178],344.6,[194,178,185,69,178,178,180,180,178],[194,178,188,546,180,178,180,180,178],2.38,[194,178,191,182,180,178,180,180,178],[194,178,194,549,180,178,180,180,178],118.5,[194,178,197,551,180,178,180,180,178],62.47,[194,178,200,553,180,178,180,180,178],21.78,[194,178,203,555,180,178,180,180,178],161.6,[194,178,210,69,178,178,180,180,178],[194,178,433,558,180,178,180,180,178],118.7,[194,178,436,69,182,178,180,180,178],[194,182,438,513,180,178,178,180,182],[197,178,178,562,180,178,180,180,178],112.8,[197,178,182,564,180,178,180,180,178],11.5,[197,178,185,566,180,178,180,180,178],123.53,[197,178,188,568,180,178,180,180,178],2.5,[197,178,191,570,180,178,180,180,178],0.81,[197,178,194,572,180,178,180,180,178],57.8,[197,178,197,574,180,178,180,180,178],54.86,[197,178,200,576,180,178,180,180,178],18.77,[197,178,203,578,180,178,180,180,178],110.49,[197,178,210,580,180,178,180,180,178],76.66,[197,178,433,582,180,178,180,180,178],13.65,[197,178,436,584,180,178,180,180,178],53.03,[197,182,438,586,180,178,178,180,182],39,[200,178,178,588,180,178,180,180,178],8.3,[200,178,182,590,180,178,180,180,178],11.86,[200,178,185,592,180,178,180,180,178],39.64,[200,178,188,568,180,178,180,180,178],[200,178,191,595,180,178,180,180,178],0.78,[200,178,194,597,180,178,180,180,178],5.74,[200,178,197,599,180,178,180,180,178],11.6,[200,178,200,601,180,178,180,180,178],1.52,[200,178,203,603,180,178,180,180,178],110.9,[200,178,210,605,180,178,180,180,178],76.7,[200,178,433,607,180,178,180,180,178],13.7,[200,178,436,609,180,178,180,180,178],25.76,[200,182,438,586,180,178,178,180,182],[612,129],"failed",[614],"Table 2(b)",[616],"NVIDIA RTX 3090 (all timing experiments, Sec. 4)",[],[619,620],"KITTI odometry sequences 00-10, monocular ATE; X = failure, '-' = average not computed; values checked against the ECCV 2024 version of record (same table numbering)","KITTI odometry sequences 00-10, monocular; FPS column; values checked against the ECCV 2024 version of record (same table numbering)",{"slug":622,"group":623,"sourceId":624,"sourceLabel":625,"table":122,"selfRows":626,"metrics":627,"seqs":630,"entrants":650,"cells":676,"outcomes":861,"locators":862,"hardware":863,"wordings":864,"notes":865},"nicerslam2024-table-3","nicerslam2024:Table 3","nicerslam2024","Zhu et al., 2024",18,[628],{"label":629,"unit":127,"statistic":128,"alignment":129},"ATE RMSE [cm]",[631,635,637,639,641,643,645,647,649],{"dataset":632,"sequence":633,"environment":634},"Replica","rm-0","synthetic indoor scenes",{"dataset":632,"sequence":636,"environment":634},"rm-1",{"dataset":632,"sequence":638,"environment":634},"rm-2",{"dataset":632,"sequence":640,"environment":634},"off-0",{"dataset":632,"sequence":642,"environment":634},"off-1",{"dataset":632,"sequence":644,"environment":634},"off-2",{"dataset":632,"sequence":646,"environment":634},"off-3",{"dataset":632,"sequence":648,"environment":634},"off-4",{"dataset":632,"sequence":152,"environment":634},[651,653,655,657,659,662,664,666,668,670,672,674],{"name":652,"methodId":160,"linkable":157,"proposed":75,"self":75},"NICE-SLAM (RGB-D input)",{"name":654,"methodId":69,"linkable":75,"proposed":75,"self":75},"Vox-Fusion (RGB-D input)",{"name":656,"methodId":69,"linkable":75,"proposed":75,"self":75},"COLMAP (RGB input)",{"name":658,"methodId":69,"linkable":75,"proposed":75,"self":75},"TANDEM (RGB input)",{"name":660,"methodId":661,"linkable":157,"proposed":75,"self":75},"DSO (RGB input)","dso2018",{"name":663,"methodId":69,"linkable":75,"proposed":75,"self":75},"Orbeez-SLAM (RGB input)",{"name":665,"methodId":69,"linkable":75,"proposed":75,"self":75},"NeRF-SLAM (RGB input)",{"name":667,"methodId":69,"linkable":75,"proposed":75,"self":75},"DIM-SLAM (RGB input)",{"name":669,"methodId":69,"linkable":75,"proposed":75,"self":75},"DIM-SLAM* (RGB input, authors' reimplementation)",{"name":671,"methodId":5,"linkable":157,"proposed":75,"self":157},"DROID-SLAM (RGB input)",{"name":673,"methodId":5,"linkable":157,"proposed":75,"self":157},"DROID-SLAM* (RGB input, no global BA or loop closure)",{"name":675,"methodId":69,"linkable":75,"proposed":157,"self":75},"NICER-SLAM (RGB input)",[677,679,681,683,685,687,689,691,693,695,697,699,701,703,705,707,709,711,713,715,717,719,721,723,725,727,729,731,733,735,736,738,739,741,742,744,746,747,749,751,753,755,757,759,760,762,764,766,767,769,770,771,773,775,777,779,781,783,785,787,789,791,793,795,797,798,800,802,804,805,806,807,808,810,812,814,815,816,817,818,820,821,822,824,825,826,828,829,831,833,834,835,836,837,838,839,840,842,843,844,846,848,850,852,854,855,857,859],[178,178,178,678,180,178,180,180,178],1.69,[178,178,182,680,180,178,180,180,178],2.04,[178,178,185,682,180,178,180,180,178],1.55,[178,178,188,684,180,178,180,180,178],0.99,[178,178,191,686,180,178,180,180,178],0.9,[178,178,194,688,180,178,180,180,178],1.39,[178,178,197,690,180,178,180,180,178],3.97,[178,178,200,692,180,178,180,180,178],3.08,[178,178,203,694,180,178,180,180,178],1.95,[182,178,178,696,180,178,180,180,178],0.27,[182,178,182,698,180,178,180,180,178],1.33,[182,178,185,700,180,178,180,180,178],0.47,[182,178,188,702,180,178,180,180,178],0.7,[182,178,191,704,180,178,180,180,178],1.11,[182,178,194,706,180,178,180,180,178],0.46,[182,178,197,708,180,178,180,180,178],0.26,[182,178,200,710,180,178,180,180,178],0.58,[182,178,203,712,180,178,180,180,178],0.65,[185,178,178,714,180,178,180,180,178],0.62,[185,178,182,716,180,178,180,180,178],23.7,[185,178,185,718,180,178,180,180,178],0.39,[185,178,188,720,180,178,180,180,178],0.33,[185,178,191,722,180,178,180,180,178],0.24,[185,178,194,724,180,178,180,180,178],0.79,[185,178,197,726,180,178,180,180,178],0.14,[185,178,200,728,180,178,180,180,178],1.73,[185,178,203,730,180,178,180,180,178],3.49,[188,178,178,732,180,178,180,180,178],0.54,[188,178,182,734,180,178,180,180,178],0.43,[188,178,185,700,180,178,180,180,178],[188,178,188,737,180,178,180,180,178],0.61,[188,178,191,720,180,178,180,180,178],[188,178,194,740,180,178,180,180,178],5.42,[188,178,197,523,180,178,180,180,178],[188,178,200,743,180,178,180,180,178],0.75,[188,178,203,745,180,178,180,180,178],1.15,[191,178,178,708,180,178,180,180,178],[191,178,182,748,180,178,180,180,178],0.25,[191,178,185,750,180,178,180,180,178],0.19,[191,178,188,752,180,178,180,180,178],0.38,[191,178,191,754,180,178,180,180,178],0.2,[191,178,194,756,180,178,180,180,178],2.53,[191,178,197,758,180,178,180,180,178],0.22,[191,178,200,752,180,178,180,180,178],[191,178,203,761,180,178,180,180,178],0.55,[194,178,178,763,180,178,180,180,178],0.34,[194,178,182,765,180,178,180,180,178],0.41,[194,178,185,696,180,178,180,180,178],[194,178,188,768,180,178,180,180,178],0.36,[194,178,191,69,178,178,180,180,178],[194,178,194,69,178,178,180,180,178],[194,178,197,772,180,178,180,180,178],0.294,[194,178,200,774,180,178,180,180,178],2.89,[194,178,203,776,180,178,180,180,178],0.76,[197,178,178,778,180,178,180,180,178],17.26,[197,178,182,780,180,178,180,180,178],11.94,[197,178,185,782,180,178,180,180,178],15.76,[197,178,188,784,180,178,180,180,178],12.75,[197,178,191,786,180,178,180,180,178],10.34,[197,178,194,788,180,178,180,180,178],14.52,[197,178,197,790,180,178,180,180,178],20.32,[197,178,200,792,180,178,180,180,178],14.96,[197,178,203,794,180,178,180,180,178],14.73,[200,178,178,796,180,178,180,180,178],0.48,[200,178,182,595,180,178,180,180,178],[200,178,185,799,180,178,180,180,178],0.35,[200,178,188,801,180,178,180,180,178],0.67,[200,178,191,803,180,178,180,180,178],0.37,[200,178,194,768,180,178,180,180,178],[200,178,197,720,180,178,180,180,178],[200,178,200,768,180,178,180,180,178],[200,178,203,706,180,178,180,180,178],[203,178,178,809,180,178,180,180,178],1.06,[203,178,182,811,180,178,180,180,178],0.49,[203,178,185,813,180,178,180,180,178],0.32,[203,178,188,734,180,178,180,180,178],[203,178,191,708,180,178,180,180,178],[203,178,194,712,180,178,180,180,178],[203,178,197,761,180,178,180,180,178],[203,178,200,819,180,178,180,180,178],3.69,[203,178,203,448,180,178,180,180,178],[210,178,178,763,180,178,180,180,178],[210,178,182,823,180,178,180,180,178],0.13,[210,178,185,696,180,178,180,180,178],[210,178,188,748,180,178,180,180,178],[210,178,191,827,180,178,180,180,178],0.42,[210,178,194,813,180,178,180,180,178],[210,178,197,830,180,178,180,180,178],0.52,[210,178,200,832,180,178,180,180,178],0.4,[210,178,203,720,180,178,180,180,178],[433,178,178,710,180,178,180,180,178],[433,178,182,710,180,178,180,180,178],[433,178,185,752,180,178,180,180,178],[433,178,188,809,180,178,180,180,178],[433,178,191,832,180,178,180,180,178],[433,178,194,702,180,178,180,180,178],[433,178,197,841,180,178,180,180,178],0.53,[433,178,200,698,180,178,180,180,178],[433,178,203,702,180,178,180,180,178],[436,178,178,845,180,178,180,180,178],1.36,[436,178,182,847,180,178,180,180,178],1.6,[436,178,185,849,180,178,180,180,178],1.14,[436,178,188,851,180,178,180,180,178],2.12,[436,178,191,853,180,178,180,180,178],3.23,[436,178,194,851,180,178,180,180,178],[436,178,197,856,180,178,180,180,178],1.42,[436,178,200,858,180,178,180,180,178],2.01,[436,178,203,860,180,178,180,180,178],1.88,[612],[122],[],[],[866],"3DV Table 3: Replica ATE RMSE; trajectories aligned to ground truth with evo (transform not stated); DIM-SLAM* is the authors reimplementation; DROID-SLAM* has no final global BA and loop closure; F = program failure or trajectory that cannot be aligned (SVD error)",{"slug":868,"group":869,"sourceId":870,"sourceLabel":871,"table":872,"selfRows":873,"metrics":874,"seqs":877,"entrants":896,"cells":911,"outcomes":1012,"locators":1013,"hardware":1014,"wordings":1015,"notes":1016},"vggtslam2025-table-1","vggtslam2025:Table 1","vggtslam2025","Maggio et al., 2025","Table 1",16,[875],{"label":876,"unit":362,"statistic":128,"alignment":129},"ATE RMSE [m]",[878,882,884,886,888,890,892,894],{"dataset":879,"sequence":880,"environment":881},"7-Scenes","chess","not described in the paper",{"dataset":879,"sequence":883,"environment":881},"fire",{"dataset":879,"sequence":885,"environment":881},"heads",{"dataset":879,"sequence":887,"environment":881},"office",{"dataset":879,"sequence":889,"environment":881},"pumpkin",{"dataset":879,"sequence":891,"environment":881},"kitchen",{"dataset":879,"sequence":893,"environment":881},"stairs",{"dataset":879,"sequence":895,"environment":881},"Avg",[897,899,900,903,905,907,909],{"name":898,"methodId":69,"linkable":75,"proposed":75,"self":75},"NICER-SLAM",{"name":7,"methodId":5,"linkable":157,"proposed":75,"self":157},{"name":901,"methodId":902,"linkable":157,"proposed":75,"self":75},"MASt3R-SLAM","mast3rslam2025",{"name":904,"methodId":5,"linkable":157,"proposed":75,"self":157},"DROID-SLAM*",{"name":906,"methodId":902,"linkable":157,"proposed":75,"self":75},"MASt3R-SLAM*",{"name":908,"methodId":69,"linkable":75,"proposed":75,"self":75},"Ours (Sim(3), w = 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