[{"data":1,"prerenderedAt":999},["ShallowReactive",2],{"method-pointslam2023":3},{"method":4,"reference":51,"equipment":72,"figures":91,"results":92},{"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":24,"sensors":31,"platform":33,"estimator":34,"association":35,"timeModel":36,"deskew":37,"loopClosure":38,"globalOptimization":39,"mapRepresentation":40,"prior":41,"outputGeometry":42,"compute":43,"codeUrl":44,"codeLicense":45,"relatedVersions":46},"pointslam2023","Sandström et al., 2023","Point-SLAM","Point-SLAM: Dense Neural Point Cloud-based SLAM",2023,"recent","C09","odometry_with_local_mapping","Point-SLAM 將神經特徵錨定在隨輸入逐步生成的點雲上，並依影像梯度動態調整點密度，細節處加密、平坦處稀疏；追蹤與建圖共用同一個以 RGB-D 重渲染誤差最佳化的點式表示。其網格評估在計算精確率與召回率前先以 ICP 對齊，因此量到的是局部形狀品質而非全域位置精度。","Anchors neural features on an input-adaptive point cloud used for both tracking and mapping in RGB-D SLAM.","full_text_reviewed","peer_reviewed_published","background","論文未涉及營建場域；資料為 Replica、TUM-RGBD、ScanNet。",[20,21],"simulation","public_benchmark",[23],"Better reconstruction and rendering accuracy than NICE-SLAM, Vox-Fusion and ESLAM on Replica (Sec. 4.1)",[25,26,27,28,29,30],"More sensitive to motion blur and specularities (Sec. 4.2; Limitations)","Point density follows a heuristic; many empirical hyperparameters (Limitations)","No loop closure; gap to traditional methods on real data (Sec. 4.2)","Point locations are not optimized on the fly, limiting robustness to depth noise (Limitations)","The non-linear appearance MLP is disabled on TUM-RGBD and ScanNet because it does not help when tracking errors are higher (Sec. 4.4)","(derived from Table 6) about 0.85 s tracking and 9.85 s mapping per frame on Replica office 0",[32],"RGB-D",[],"gradient-based minimization of an RGB-D re-rendering loss for tracking and mapping, run as separate alternating processes; tracking initialised with a constant-speed assumption; mapping iterations adapted to the number of newly added points; optional exposure-compensation MLP for ScanNet","direct colour and depth re-rendering loss","discrete poses","not_applicable","none (authors note a gap to traditional methods with loop closures)","none","neural point cloud: features anchored at points with density adapted to image-gradient information","uses the pretrained and fixed middle-level geometric decoder provided by NICE-SLAM; colour decoder, interpolation MLP and point features are optimized online","mesh produced by rendering depth and colour every fifth frame along the estimated trajectory, TSDF Fusion at 1 cm voxels and marching cubes; evaluated with precision, recall and F-score at a 1 cm threshold after ICP alignment to the GT mesh; rendered depth and colour images","runtime profiled on a single NVIDIA RTX 2080 Ti; experiments on various NVIDIA GPUs with at most 12 GB memory (Sec. 4.4; App. C); on Replica office 0: 21 ms tracking and 33 ms mapping per iteration, 0.85 s tracking and 9.85 s mapping per frame, 27.23 MB scene embedding (Table 6); [photoslam2024] Table 1 measured 0.345 tracking FPS and more than 2 h operation time on Replica RGB-D with an RTX 4090","https:\u002F\u002Fgithub.com\u002Feriksandstroem\u002FPoint-SLAM","Apache-2.0",[47],{"relation":48,"title":49,"doi_or_url":50},"preprint","arXiv:2304.04278","https:\u002F\u002Farxiv.org\u002Fabs\u002F2304.04278",{"id":5,"kind":52,"shortName":7,"title":8,"authors":53,"year":9,"venue":58,"venueType":59,"publisher":60,"volumeIssuePages":61,"doi":62,"arxivId":63,"url":64,"firstPublicDate":65,"publicationStatus":16,"metadataStatus":66,"fulltextStatus":15,"era":10,"classicReason":37,"codeUrl":44,"cluster":11,"topics":67,"mdpi":68,"verification":69,"label":6,"fulltextRoute":70,"versionRead":71,"addedByCensus":68},"method",[54,55,56,57],"Erik Sandström","Yue Li","Luc Van Gool","Martin R. Oswald","2023 IEEE\u002FCVF International Conference on Computer Vision (ICCV)","conference","IEEE","pp. 18387-18398","10.1109\u002Ficcv51070.2023.01690","2304.04278","https:\u002F\u002Fapi.crossref.org\u002Fworks\u002F10.1109\u002Ficcv51070.2023.01690","2023-04-09","metadata_verified",[11],false,"confirmed","arXiv","arXiv v3 (2023-09-12) read in full including supplementary App. A to E; CVF open-access ICCV 2023 main paper also read: Tables 1 to 4, Table 6, Fig. 3a values, hardware and limitation text identical to arXiv v3",[73,80,84],{"category":74,"model":75,"canonical":75,"role":76,"dataset":77,"specs":78,"locator":79},"compute","NVIDIA RTX 2080 Ti","compute for runtime",null,"single GPU used to profile Point-SLAM and NICE-SLAM runtimes","Sec. 4.4 Memory and Runtime Analysis",{"category":74,"model":81,"canonical":81,"role":76,"dataset":77,"specs":82,"locator":83},"NVIDIA RTX 3090","GPU used for the Vox-Fusion runtime in Table 6","Sec. 4.4",{"category":85,"model":86,"canonical":86,"role":87,"dataset":88,"specs":89,"locator":90},"other","external motion capture system (model not named)","reference or ground truth","TUM-RGBD","provides TUM-RGBD ground-truth poses","Sec. 4 Datasets",[],{"totalRows":93,"groupCount":94,"groups":95,"others":843},169,36,[96,474,621,728],{"slug":97,"group":98,"sourceId":99,"sourceLabel":100,"table":101,"selfRows":102,"metrics":103,"seqs":109,"entrants":163,"cells":191,"outcomes":466,"locators":468,"hardware":469,"wordings":470,"notes":471},"splatam2024-table-1","splatam2024:Table 1","splatam2024","Keetha et al., 2024","Table 1",25,[104],{"label":105,"unit":106,"statistic":107,"alignment":108},"ATE RMSE [cm]","cm","RMSE","not_reported",[110,114,116,118,121,123,125,127,129,131,133,135,137,139,141,143,145,147,149,151,153,155,157,159,161],{"dataset":111,"sequence":112,"environment":113},"ScanNet++","Avg.","high-quality DSLR colour and depth captures with very large inter-frame motion",{"dataset":111,"sequence":115,"environment":113},"S1 (8b5caf3398)",{"dataset":111,"sequence":117,"environment":113},"S2 (b20a261fdf)",{"dataset":119,"sequence":112,"environment":120},"Replica","synthetic scenes",{"dataset":119,"sequence":122,"environment":120},"room0",{"dataset":119,"sequence":124,"environment":120},"room1",{"dataset":119,"sequence":126,"environment":120},"room2",{"dataset":119,"sequence":128,"environment":120},"office0",{"dataset":119,"sequence":130,"environment":120},"office1",{"dataset":119,"sequence":132,"environment":120},"office2",{"dataset":119,"sequence":134,"environment":120},"office3",{"dataset":119,"sequence":136,"environment":120},"office4",{"dataset":88,"sequence":112,"environment":138},"real RGB-D sequences from old low-quality cameras (sparse depth, strong motion blur)",{"dataset":88,"sequence":140,"environment":138},"fr1\u002Fdesk",{"dataset":88,"sequence":142,"environment":138},"fr1\u002Fdesk2",{"dataset":88,"sequence":144,"environment":138},"fr1\u002Froom",{"dataset":88,"sequence":146,"environment":138},"fr2\u002Fxyz",{"dataset":88,"sequence":148,"environment":138},"fr3\u002Foffice",{"dataset":150,"sequence":112,"environment":138},"ScanNet (original)",{"dataset":150,"sequence":152,"environment":138},"0000",{"dataset":150,"sequence":154,"environment":138},"0059",{"dataset":150,"sequence":156,"environment":138},"0106",{"dataset":150,"sequence":158,"environment":138},"0169",{"dataset":150,"sequence":160,"environment":138},"0181",{"dataset":150,"sequence":162,"environment":138},"0207",[164,166,169,171,174,176,179,182,185,188],{"name":7,"methodId":5,"linkable":165,"proposed":68,"self":165},true,{"name":167,"methodId":168,"linkable":165,"proposed":68,"self":68},"ORB-SLAM3","orbslam3_2021",{"name":170,"methodId":99,"linkable":165,"proposed":165,"self":68},"SplaTAM",{"name":172,"methodId":173,"linkable":165,"proposed":68,"self":68},"DROID-SLAM","droidslam2021",{"name":175,"methodId":77,"linkable":68,"proposed":68,"self":68},"Vox-Fusion",{"name":177,"methodId":178,"linkable":165,"proposed":68,"self":68},"NICE-SLAM","niceslam2022",{"name":180,"methodId":181,"linkable":165,"proposed":68,"self":68},"ESLAM","eslam2023",{"name":183,"methodId":184,"linkable":165,"proposed":68,"self":68},"Kintinuous","kintinuous2015",{"name":186,"methodId":187,"linkable":165,"proposed":68,"self":68},"ElasticFusion","elasticfusion2015",{"name":189,"methodId":190,"linkable":165,"proposed":68,"self":68},"ORB-SLAM2","orbslam2_2017",[192,196,199,202,204,206,208,210,212,214,217,220,222,225,228,231,234,237,240,242,244,246,248,250,252,254,256,258,260,262,264,266,268,269,270,272,274,276,278,280,282,284,286,288,290,291,292,294,296,298,299,301,303,305,306,307,309,311,313,315,317,318,320,321,324,327,330,333,336,338,340,342,344,346,348,350,352,354,356,357,358,359,361,363,365,367,369,371,373,375,376,378,380,382,384,386,388,390,391,393,395,397,399,401,403,405,408,411,414,417,420,423,426,428,429,430,432,434,436,438,440,442,444,446,448,450,452,454,456,458,460,462,464],[193,193,193,194,193,193,195,195,193],0,343.8,-1,[193,193,197,198,193,193,195,195,193],1,296.7,[193,193,200,201,193,193,195,195,193],2,390.8,[197,193,193,203,193,193,195,195,193],158.2,[197,193,197,205,193,193,195,195,193],156.8,[197,193,200,207,193,193,195,195,193],159.7,[200,193,193,209,195,193,195,195,193],1.2,[200,193,197,211,195,193,195,195,193],0.6,[200,193,200,213,195,193,195,195,193],1.9,[215,193,215,216,195,193,195,195,197],3,0.38,[215,193,218,219,195,193,195,195,197],4,0.53,[215,193,221,216,195,193,195,195,197],5,[215,193,223,224,195,193,195,195,197],6,0.45,[215,193,226,227,195,193,195,195,197],7,0.35,[215,193,229,230,195,193,195,195,197],8,0.24,[215,193,232,233,195,193,195,195,197],9,0.36,[215,193,235,236,195,193,195,195,197],10,0.33,[215,193,238,239,195,193,195,195,197],11,0.43,[218,193,215,241,195,193,195,195,197],3.09,[218,193,218,243,195,193,195,195,197],1.37,[218,193,221,245,195,193,195,195,197],4.7,[218,193,223,247,195,193,195,195,197],1.47,[218,193,226,249,195,193,195,195,197],8.48,[218,193,229,251,195,193,195,195,197],2.04,[218,193,232,253,195,193,195,195,197],2.58,[218,193,235,255,195,193,195,195,197],1.11,[218,193,238,257,195,193,195,195,197],2.94,[221,193,215,259,195,193,195,195,197],1.06,[221,193,218,261,195,193,195,195,197],0.97,[221,193,221,263,195,193,195,195,197],1.31,[221,193,223,265,195,193,195,195,197],1.07,[221,193,226,267,195,193,195,195,197],0.88,[221,193,229,197,195,193,195,195,197],[221,193,232,259,195,193,195,195,197],[221,193,235,271,195,193,195,195,197],1.1,[221,193,238,273,195,193,195,195,197],1.13,[223,193,215,275,195,193,195,195,197],0.63,[223,193,218,277,195,193,195,195,197],0.71,[223,193,221,279,195,193,195,195,197],0.7,[223,193,223,281,195,193,195,195,197],0.52,[223,193,226,283,195,193,195,195,197],0.57,[223,193,229,285,195,193,195,195,197],0.55,[223,193,232,287,195,193,195,195,197],0.58,[223,193,235,289,195,193,195,195,197],0.72,[223,193,238,275,195,193,195,195,197],[193,193,215,281,195,193,195,195,197],[193,193,218,293,195,193,195,195,197],0.61,[193,193,221,295,195,193,195,195,197],0.41,[193,193,223,297,195,193,195,195,197],0.37,[193,193,226,216,195,193,195,195,197],[193,193,229,300,195,193,195,195,197],0.48,[193,193,232,302,195,193,195,195,197],0.54,[193,193,235,304,195,193,195,195,197],0.69,[193,193,238,289,195,193,195,195,197],[200,193,215,233,195,193,195,195,197],[200,193,218,308,195,193,195,195,197],0.31,[200,193,221,310,195,193,195,195,197],0.4,[200,193,223,312,195,193,195,195,197],0.29,[200,193,226,314,195,193,195,195,197],0.47,[200,193,229,316,195,193,195,195,197],0.27,[200,193,232,312,195,193,195,195,197],[200,193,235,319,195,193,195,195,197],0.32,[200,193,238,285,195,193,195,195,197],[226,193,322,323,195,193,195,195,197],12,4.84,[226,193,325,326,195,193,195,195,197],13,3.7,[226,193,328,329,195,193,195,195,197],14,7.1,[226,193,331,332,195,193,195,195,197],15,7.5,[226,193,334,335,195,193,195,195,197],16,2.9,[226,193,337,215,195,193,195,195,197],17,[229,193,322,339,195,193,195,195,197],6.91,[229,193,325,341,195,193,195,195,197],2.53,[229,193,328,343,195,193,195,195,197],6.83,[229,193,331,345,195,193,195,195,197],21.49,[229,193,334,347,195,193,195,195,197],1.17,[229,193,337,349,195,193,195,195,197],2.52,[232,193,322,351,195,193,195,195,197],1.98,[232,193,325,353,195,193,195,195,197],1.6,[232,193,328,355,195,193,195,195,197],2.2,[232,193,331,245,195,193,195,195,197],[232,193,334,310,195,193,195,195,197],[232,193,337,197,195,193,195,195,197],[221,193,322,360,195,193,195,195,197],15.87,[221,193,325,362,195,193,195,195,197],4.26,[221,193,328,364,195,193,195,195,197],4.99,[221,193,331,366,195,193,195,195,197],34.49,[221,193,334,368,195,193,195,195,197],31.73,[221,193,337,370,195,193,195,195,197],3.87,[218,193,322,372,195,193,195,195,197],11.31,[218,193,325,374,195,193,195,195,197],3.52,[218,193,328,223,195,193,195,195,197],[218,193,331,377,195,193,195,195,197],19.53,[218,193,334,379,195,193,195,195,197],1.49,[218,193,337,381,195,193,195,195,197],26.01,[193,193,322,383,195,193,195,195,197],8.92,[193,193,325,385,195,193,195,195,197],4.34,[193,193,328,387,195,193,195,195,197],4.54,[193,193,331,389,195,193,195,195,197],30.92,[193,193,334,263,195,193,195,195,197],[193,193,337,392,195,193,195,195,197],3.48,[200,193,322,394,195,193,195,195,197],5.48,[200,193,325,396,195,193,195,195,197],3.35,[200,193,328,398,195,193,195,195,197],6.54,[200,193,331,400,195,193,195,195,197],11.13,[200,193,334,402,195,193,195,195,197],1.24,[200,193,337,404,195,193,195,195,197],5.16,[218,193,406,407,195,193,195,195,197],18,26.9,[218,193,409,410,195,193,195,195,197],19,68.84,[218,193,412,413,195,193,195,195,197],20,24.18,[218,193,415,416,195,193,195,195,197],21,8.41,[218,193,418,419,195,193,195,195,197],22,27.28,[218,193,421,422,195,193,195,195,197],23,23.3,[218,193,424,425,195,193,195,195,197],24,9.41,[221,193,406,427,195,193,195,195,197],10.7,[221,193,409,322,195,193,195,195,197],[221,193,412,328,195,193,195,195,197],[221,193,415,431,195,193,195,195,197],7.9,[221,193,418,433,195,193,195,195,197],10.9,[221,193,421,435,195,193,195,195,197],13.4,[221,193,424,437,195,193,195,195,197],6.2,[193,193,406,439,195,193,195,195,197],12.19,[193,193,409,441,195,193,195,195,197],10.24,[193,193,412,443,195,193,195,195,197],7.81,[193,193,415,445,195,193,195,195,197],8.65,[193,193,418,447,195,193,195,195,197],22.16,[193,193,421,449,195,193,195,195,197],14.77,[193,193,424,451,195,193,195,195,197],9.54,[200,193,406,453,195,193,195,195,197],11.88,[200,193,409,455,195,193,195,195,197],12.83,[200,193,412,457,195,193,195,195,197],10.1,[200,193,415,459,195,193,195,195,197],17.72,[200,193,418,461,195,193,195,195,197],12.08,[200,193,421,463,195,193,195,195,197],11.1,[200,193,424,465,195,193,195,195,197],7.46,[467],"failed (tracking failure reported in Sec. 5)",[101],[],[],[472,473],"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":475,"group":476,"sourceId":477,"sourceLabel":478,"table":101,"selfRows":232,"metrics":479,"seqs":481,"entrants":501,"cells":520,"outcomes":612,"locators":613,"hardware":615,"wordings":616,"notes":617},"gsicpslam2024-table-1","gsicpslam2024:Table 1","gsicpslam2024","Ha et al., 2024",[480],{"label":105,"unit":106,"statistic":107,"alignment":108},[482,485,487,489,491,493,495,497,499],{"dataset":119,"sequence":483,"environment":484},"R0","synthetic indoor scenes",{"dataset":119,"sequence":486,"environment":484},"R1",{"dataset":119,"sequence":488,"environment":484},"R2",{"dataset":119,"sequence":490,"environment":484},"Of0",{"dataset":119,"sequence":492,"environment":484},"Of1",{"dataset":119,"sequence":494,"environment":484},"Of2",{"dataset":119,"sequence":496,"environment":484},"Of3",{"dataset":119,"sequence":498,"environment":484},"Of4",{"dataset":119,"sequence":500,"environment":484},"average of 8 scenes",[502,504,506,508,511,513,515,518],{"name":503,"methodId":178,"linkable":165,"proposed":68,"self":68},"NICE-SLAM* [ 47 ]",{"name":505,"methodId":5,"linkable":165,"proposed":68,"self":165},"Point-SLAM* [ 32 ]",{"name":507,"methodId":77,"linkable":68,"proposed":68,"self":68},"GS-SLAM [ 44 ]",{"name":509,"methodId":510,"linkable":165,"proposed":68,"self":68},"Photo-SLAM [ 12 ]","photoslam2024",{"name":512,"methodId":99,"linkable":165,"proposed":68,"self":68},"SplaTAM* [ 14 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