[{"data":1,"prerenderedAt":790},["ShallowReactive",2],{"method-eslam2023":3},{"method":4,"reference":52,"equipment":72,"figures":80,"results":118},{"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":28,"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},"eslam2023","Johari et al., 2023","ESLAM","ESLAM: Efficient Dense SLAM System Based on Hybrid Representation of Signed Distance Fields",2023,"recent","C09","odometry_with_local_mapping","ESLAM 以多尺度軸對齊特徵平面（tri-plane）取代體素網格，使記憶體隨場景邊長由立方成長降為平方成長，並直接解碼截斷符號距離場（TSDF）以加速收斂。作者承認特徵平面的更新可能影響已重建區域，因此需投入大量計算重播舊關鍵影格以避免遺忘。","Tri-plane features decoded to TSDF reduce memory growth from cubic to quadratic and speed up neural RGB-D SLAM, at the cost of forgetting management.","full_text_reviewed","peer_reviewed_published","supplementary","論文未涉及營建場域；資料為 Replica、ScanNet、TUM RGB-D。",[20,21],"simulation","public_benchmark",[23,24,25,26,27],"Memory growth quadratic in scene side length (Sec. 3.1)","Faster than NICE-SLAM by up to an order of magnitude (Sec. 4.2)","Replica averages: ATE RMSE 0.63 cm, Acc. 0.97 cm, Comp. 1.05 cm, completion ratio 98.60% versus NICE-SLAM 2.05 cm, 1.66 cm, 1.63 cm and 96.74% (Table 1)","ScanNet average ATE RMSE 7.4 cm versus 10.7 cm for NICE-SLAM, with lower run-to-run variance (Table 2)","Little sensitivity to 1\u002F8 depth resolution on Replica room0 (ATE 0.72 versus 0.71 cm) (Supp. Table 1)",[29,30,31],"Updating plane features can alter previously reconstructed geometry (forgetting); compute spent on replay (Sec. 5)","Requires depth: SDF and depth losses apply only to rays with measured depth (Sec. 3.3)","No quantitative reconstruction on real data: ScanNet ground-truth meshes are incomplete and TUM has none (Sec. 4.1)",[33],"RGB-D",[],"Adam gradient-based tracking of translation + quaternion; periodic joint mapping over keyframes","per-point TSDF (free-space and truncation) losses + depth and colour rendering losses","discrete poses","not_applicable","none reported","no global BA, pose graph or loop closure; every 4 input frames a mapping step jointly optimizes feature planes, decoders and the poses of W = 20 frames (current frame, two previous keyframes and 17 keyframes sampled at random from the global keyframe list)","multi-scale axis-aligned feature planes (tri-plane) with shallow decoders to TSDF and RGB","none (pre-train-free)","mesh via marching cubes on a 1 cm TSDF volume, with frustum\u002Focclusion mesh culling before evaluation","NVIDIA GeForce RTX 3090; average frame processing time 0.18 s on Replica room0 (NICE-SLAM 2.10 s) and 0.55 s on ScanNet scene0000 (NICE-SLAM 3.35 s); 6.79 M and 17.63 M parameters (Table 4); up to 10x faster than NICE-SLAM","https:\u002F\u002Fgithub.com\u002Fidiap\u002FESLAM","Apache-2.0",[48],{"relation":49,"title":50,"doi_or_url":51},"preprint","arXiv:2211.11704","https:\u002F\u002Farxiv.org\u002Fabs\u002F2211.11704",{"id":5,"kind":53,"shortName":7,"title":8,"authors":54,"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":38,"codeUrl":45,"cluster":11,"topics":67,"mdpi":68,"verification":69,"label":6,"fulltextRoute":70,"versionRead":71,"addedByCensus":68},"method",[55,56,57],"Mohammad Mahdi Johari","Camilla Carta","François Fleuret","2023 IEEE\u002FCVF Conference on Computer Vision and Pattern Recognition (CVPR)","conference","IEEE","pp. 17408-17419","10.1109\u002Fcvpr52729.2023.01670","2211.11704","https:\u002F\u002Fapi.crossref.org\u002Fworks\u002F10.1109\u002Fcvpr52729.2023.01670","2022-11-21","metadata_verified",[11],false,"confirmed","arXiv","arXiv v2 (2211.11704v2, 3 Apr 2023) including supplementary material; CVPR 2023 version of record not compared",[73],{"category":74,"model":75,"canonical":75,"role":76,"dataset":77,"specs":78,"locator":79},"compute","NVIDIA GeForce RTX 3090","compute for runtime",null,"used to benchmark all methods in Table 4","Table 4; Sec. 4.2",[81,94,102,110],{"refId":5,"refLabel":6,"fig":82,"whatZh":83,"license":84,"licenseUrl":85,"sourceUrl":86,"src":87,"width":88,"height":89,"thumb":90,"thumbWidth":91,"thumbHeight":92,"modified":93},"Fig. 2","ESLAM 總覽：沿光線取樣點投影到粗細兩層軸對齊特徵平面，經淺層解碼器輸出 TSDF 與顏色並做 SDF 體積渲染","CC BY 4.0","https:\u002F\u002Fcreativecommons.org\u002Flicenses\u002Fby\u002F4.0\u002F","https:\u002F\u002Farxiv.org\u002Fhtml\u002F2211.11704v2\u002FFigures\u002FFig2\u002FFig2.jpg","\u002Ffigure-files\u002Feslam2023\u002Ffig-2.webp",1400,751,"\u002Ffigure-files\u002Feslam2023\u002Ffig-2.thumb.webp",480,257,"resized to at most 1400 px wide and converted to WebP",{"refId":5,"refLabel":6,"fig":95,"whatZh":96,"license":84,"licenseUrl":85,"sourceUrl":97,"src":98,"width":88,"height":99,"thumb":100,"thumbWidth":91,"thumbHeight":101,"modified":93},"Fig. 3","Replica 資料集幾何重建與 iMAP*、NICE-SLAM 的定性比較","https:\u002F\u002Farxiv.org\u002Fhtml\u002F2211.11704v2\u002FFigures\u002FFig3\u002FFig3.jpg","\u002Ffigure-files\u002Feslam2023\u002Ffig-3.webp",679,"\u002Ffigure-files\u002Feslam2023\u002Ffig-3.thumb.webp",233,{"refId":5,"refLabel":6,"fig":103,"whatZh":104,"license":84,"licenseUrl":85,"sourceUrl":105,"src":106,"width":88,"height":107,"thumb":108,"thumbWidth":91,"thumbHeight":109,"modified":93},"Fig. 4","ScanNet 相機軌跡比較，真值為綠色、估計為紅色","https:\u002F\u002Farxiv.org\u002Fhtml\u002F2211.11704v2\u002FFigures\u002FFig4\u002FFig4.jpg","\u002Ffigure-files\u002Feslam2023\u002Ffig-4.webp",863,"\u002Ffigure-files\u002Feslam2023\u002Ffig-4.thumb.webp",296,{"refId":5,"refLabel":6,"fig":111,"whatZh":112,"license":84,"licenseUrl":85,"sourceUrl":113,"src":114,"width":88,"height":115,"thumb":116,"thumbWidth":91,"thumbHeight":117,"modified":93},"Fig. 5","ScanNet 場景重建網格與 iMAP*、NICE-SLAM 比較（真值網格不完整處呈白色）","https:\u002F\u002Farxiv.org\u002Fhtml\u002F2211.11704v2\u002FFigures\u002FFig5\u002FFig5.jpg","\u002Ffigure-files\u002Feslam2023\u002Ffig-5.webp",669,"\u002Ffigure-files\u002Feslam2023\u002Ffig-5.thumb.webp",229,{"totalRows":119,"groupCount":120,"groups":121,"others":616},175,39,[122,259,337,480],{"slug":123,"group":124,"sourceId":5,"sourceLabel":6,"table":125,"selfRows":126,"metrics":127,"seqs":136,"entrants":153,"cells":163,"outcomes":253,"locators":254,"hardware":255,"wordings":256,"notes":257},"eslam2023-table-2","eslam2023:Table 2","Table 2",14,[128,133],{"label":129,"unit":130,"statistic":131,"alignment":132},"ATE Mean (cm)","cm","mean","not_reported",{"label":134,"unit":130,"statistic":135,"alignment":132},"ATE RMSE (cm)","RMSE",[137,141,143,145,147,149,151],{"dataset":138,"sequence":139,"environment":140},"ScanNet","scene0000","real indoor 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4 timings: NVIDIA GeForce RTX 3090)",[],[336],"Accuracy versus frame processing time with more optimization iterations, Replica averages",{"slug":338,"group":339,"sourceId":340,"sourceLabel":341,"table":342,"selfRows":245,"metrics":343,"seqs":346,"entrants":365,"cells":380,"outcomes":474,"locators":475,"hardware":476,"wordings":477,"notes":478},"gsslam2024-table-1","gsslam2024:Table 1","gsslam2024","Yan et al., 2024","Table 1",[344],{"label":345,"unit":130,"statistic":135,"alignment":132},"ATE RMSE [cm]",[347,350,352,354,356,358,360,362,364],{"dataset":275,"sequence":348,"environment":349},"Rm0","synthetic indoor 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ATE RMSE, 8 scenes; * = reproduced with official code; methods in upper part run below 5 FPS",{"slug":481,"group":482,"sourceId":483,"sourceLabel":484,"table":125,"selfRows":245,"metrics":485,"seqs":489,"entrants":509,"cells":523,"outcomes":610,"locators":611,"hardware":612,"wordings":613,"notes":614},"monogs2024-table-2","monogs2024:Table 2","monogs2024","Matsuki et al., 2024",[486],{"label":487,"unit":130,"statistic":135,"alignment":488},"ATE RMSE (keyframes)","SE3",[490,493,495,497,499,501,503,505,507],{"dataset":275,"sequence":491,"environment":492},"room0","synthetic Replica sequences (room0 to room2, office0 to office4) with purely rotational camera motion, RGB-D 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