[{"data":1,"prerenderedAt":502},["ShallowReactive",2],{"method-wang2025planarmesh":3},{"method":4,"reference":52,"equipment":75,"figures":96,"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":23,"limitations":25,"sensors":31,"platform":33,"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},"wang2025planarmesh","Wang et al., 2025a","PlanarMesh","PlanarMesh: Building Compact 3D Meshes from LiDAR using Incremental Adaptive Resolution Reconstruction",2025,"recent","C12","map_representation_or_reconstruction","PlanarMesh 以「平面網格」表示場景：每個元素由一個平面（位置與法向量，以增量 PCA 更新）和落在該平面上的三角網格組成，頂點半徑近似局部曲率。每個新點經兩棵可動態插入的包圍體階層樹查詢：面相交搜尋（FIS）找出射線穿過的網格面以引入自由空間資訊，反向半徑搜尋（RRS）找出半徑涵蓋該點的邊界頂點；再以 95% 信賴區間的 z 檢定判定點在平面前方、平面上或後方，據以執行更新、擴張、新增、刪除、收縮與合併。輸出前依頂點半徑重新取樣並以 Delaunay 重新三角化，因此大平面用少量大三角形、細部用小三角形。位姿由外部 LiDAR 里程計提供。","An incremental LiDAR meshing system whose planar-mesh elements (a plane plus a mesh lying on it) are updated, grown, created, carved by free-space rays, shrunk and merged, with resolution set by per-vertex radii that track local curvature; poses come from an external odometry.","full_text_reviewed","peer_reviewed_published","supplementary","以 Oxford Spires 資料集的 Christ Church 03、Keble College 03 與 Observatory 01 三條室內外序列評估，真值為高精度 TLS 點雲地圖，屬既有建築而非施工中工地。作者指出建成環境以平面為主，並認為此表示可支援平面圖生成與 BIM，但本文沒有實作（Sec. III-B）。未在營建場域驗證。",[20,21,22],"public_benchmark","completed_building","independent_reference",[24],"Accuracy on par with or better than ImMesh, VDBFusion and OctoMap on Oxford Spires against TLS ground truth, with meshes of 280-550 K faces versus 1.8-23 M and about 10 MB versus about 100 MB of accumulated LiDAR points (abstract, Sec. IV-B\u002FC, Table I).",[26,27,28,29,30],"Memory grows until it exceeds available memory at about 300 scans (about 300 m at 1 m\u002Fs walking); submapping is future work (Sec. V).","Real-time rate relies on all CPU cores while baselines were run on a single core, so runtime comparisons are not like-for-like (Sec. IV-A).","Meshes show small holes and rugged edges; smoothing or plane intersection left for future work (Sec. IV-B).","Deliberately biased toward precision over recall because tiny faces are dropped; recall 0.894 on Keble College 03 was the lowest of all methods (Sec. IV-B, Table I).","No loop closure; listed as future work (Sec. V).",[32],"3D LiDAR",[34],"not named in the paper; the text refers to walking speeds (a new scan every 0.5-1 m at normal walking speed; about 300 m at 1 m\u002Fs), which implies a person-carried scanner (inference)","not_applicable (poses from a separate LiDAR odometry such as FastLIO or VILENS; evaluation used ground-truth poses from registering each undistorted scan to the TLS map)","per-point Face Intersection Search and Reverse Radius Search on bounding volume hierarchies to find candidate planar-meshes","not_applicable (externally supplied poses)","delegated to the upstream LiDAR odometry (motion correction assumed in the supplied poses\u002Fscans)","none (elastic deformation for future loop closure mentioned as future work)","none","planar-mesh (plane models combined with adaptive-resolution mesh), stored with a bounding volume hierarchy","external poses","compact triangle mesh saved as binary PLY with 280-550 K faces (about 10 MB) on the tested sequences; before output each planar-mesh is resampled by vertex radius and re-triangulated with Delaunay, restoring concavity by discarding faces outside the original (Sec. III-F, IV-C)","CPU only; about 0.4 s per scan on average (0.7 s peak), about 2 Hz, using all cores of a 28-core Intel i7 while baselines ran single-core at about 1 Hz (Sec. IV-A, IV-D)","https:\u002F\u002Fgithub.com\u002Fori-drs\u002Fplanar_mesh","GPL-3.0 (repository LICENSE.txt)",[48],{"relation":49,"title":50,"doi_or_url":51},"preprint","arXiv:2510.13599","https:\u002F\u002Farxiv.org\u002Fabs\u002F2510.13599",{"id":5,"kind":53,"shortName":7,"title":8,"authors":54,"year":9,"venue":61,"venueType":62,"publisher":63,"volumeIssuePages":64,"doi":65,"arxivId":66,"url":51,"firstPublicDate":67,"publicationStatus":16,"metadataStatus":68,"fulltextStatus":15,"era":10,"classicReason":69,"codeUrl":45,"cluster":11,"topics":70,"mdpi":71,"verification":72,"label":6,"fulltextRoute":73,"versionRead":74,"addedByCensus":71},"method",[55,56,57,58,59,60],"Jiahao Wang","Nived Chebrolu","Yifu Tao","Lintong Zhang","Ayoung Kim","Maurice Fallon","2025 IEEE\u002FRSJ International Conference on Intelligent Robots and Systems (IROS)","conference","IEEE","pp. 15726-15733","10.1109\u002Firos60139.2025.11246204","2510.13599","2025-10-15","metadata_verified","not_applicable",[11],false,"corrected","arXiv","arXiv 2510.13599v1 (8 pp., 15 Oct 2025, CC BY 4.0; the only arXiv version) read in full; the IEEE IROS 2025 version of record (pp. 15726-15733) was not compared in this pass",[76,83,89],{"category":77,"model":78,"canonical":78,"role":79,"dataset":80,"specs":81,"locator":82},"lidar","64-beam LiDAR (model not named)","dataset sensor","Oxford Spires","beam count given only in the abstract, which ties the about 2 Hz rate to a 64-beam sensor; model not named; experiments used undistorted scans with ground-truth poses from registration to the TLS map (Sec. IV-A)","Abstract; Sec. III-A; Sec. IV-A",{"category":84,"model":85,"canonical":85,"role":86,"dataset":80,"specs":87,"locator":88},"tls_scanner","TLS (instrument not named)","reference or ground truth","highly accurate TLS point cloud map; every undistorted scan registered to it to obtain ground-truth poses; also the accuracy reference","Sec. IV-A; Fig. 6",{"category":90,"model":91,"canonical":91,"role":92,"dataset":93,"specs":94,"locator":95},"compute","28-core Intel i7 CPU","compute for runtime",null,"no GPU acceleration; PlanarMesh used all cores, baselines a single core","Sec. IV-A",[97,110],{"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","以平面網格重建畫框、整個房間與內凹窗框，不同平面以不同顏色表示","CC BY 4.0","http:\u002F\u002Fcreativecommons.org\u002Flicenses\u002Fby\u002F4.0\u002F","https:\u002F\u002Farxiv.org\u002Fhtml\u002F2510.13599v1\u002Fhero_figure_2.png","\u002Ffigure-files\u002Fwang2025planarmesh\u002Ffig-1.webp",1400,350,"\u002Ffigure-files\u002Fwang2025planarmesh\u002Ffig-1.thumb.webp",480,120,"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":115,"thumb":116,"thumbWidth":107,"thumbHeight":117,"modified":109},"Fig. 6","四段場景中 VDBFusion、ImMesh、TLS 真值與 PlanarMesh 的重建比較，標出門框與凹窗等細節","https:\u002F\u002Farxiv.org\u002Fhtml\u002F2510.13599v1\u002Fcompare_results.png","\u002Ffigure-files\u002Fwang2025planarmesh\u002Ffig-6.webp",848,"\u002Ffigure-files\u002Fwang2025planarmesh\u002Ffig-6.thumb.webp",291,{"totalRows":119,"groupCount":120,"groups":121,"others":501},57,3,[122,226,475],{"slug":123,"group":124,"sourceId":5,"sourceLabel":6,"table":125,"selfRows":126,"metrics":127,"seqs":149,"entrants":153,"cells":163,"outcomes":219,"locators":220,"hardware":221,"wordings":223,"notes":224},"wang2025planarmesh-table-ii","wang2025planarmesh:Table II","Table II",28,[128,132,135,139,142,145,147],{"label":129,"unit":130,"statistic":131,"alignment":40},"Time (s) per scan","s","not_reported",{"label":133,"unit":134,"statistic":131,"alignment":40},"File size (MB)","MB",{"label":136,"unit":137,"statistic":138,"alignment":40},"Mean distance to TLS ground truth (m)","m","mean",{"label":140,"unit":137,"statistic":141,"alignment":40},"Std of distance to TLS ground truth (m)","std",{"label":143,"unit":144,"statistic":131,"alignment":40},"Precision at 0.1 m","fraction",{"label":146,"unit":144,"statistic":131,"alignment":40},"Recall at 0.1 m",{"label":148,"unit":144,"statistic":131,"alignment":40},"F-Score at 0.1 m",[150],{"dataset":80,"sequence":151,"environment":152},"Christ Church 03","existing building, walking survey",[154,157,159,161],{"name":155,"methodId":5,"linkable":156,"proposed":156,"self":156},"PlanarMesh, seed planar-meshes kept for 0 scans",true,{"name":158,"methodId":5,"linkable":156,"proposed":156,"self":156},"PlanarMesh, seed planar-meshes kept for 1 scans",{"name":160,"methodId":5,"linkable":156,"proposed":156,"self":156},"PlanarMesh, seed planar-meshes kept for 10 scans",{"name":162,"methodId":5,"linkable":156,"proposed":156,"self":156},"PlanarMesh, seed planar-meshes kept for All scans",[164,168,171,174,176,179,182,185,187,189,191,193,195,196,198,200,202,203,204,205,206,207,209,211,212,214,215,217],[165,165,165,166,167,165,165,167,165],0,0.295,-1,[165,169,165,170,167,165,167,167,165],1,5.9,[165,172,165,173,167,165,167,167,165],2,0.037,[165,120,165,175,167,165,167,167,165],0.075,[165,177,165,178,167,165,167,167,165],4,0.946,[165,180,165,181,167,165,167,167,165],5,0.889,[165,183,165,184,167,165,167,167,165],6,0.917,[169,165,165,186,167,165,165,167,165],0.347,[169,169,165,188,167,165,167,167,165],7.9,[169,172,165,190,167,165,167,167,165],0.036,[169,120,165,192,167,165,167,167,165],0.076,[169,177,165,194,167,165,167,167,165],0.951,[169,180,165,178,167,165,167,167,165],[169,183,165,197,167,165,167,167,165],0.948,[172,165,165,199,167,165,165,167,165],0.384,[172,169,165,201,167,165,167,167,165],8.3,[172,172,165,190,167,165,167,167,165],[172,120,165,192,167,165,167,167,165],[172,177,165,194,167,165,167,167,165],[172,180,165,194,167,165,167,167,165],[172,183,165,194,167,165,167,167,165],[120,165,165,208,167,165,165,167,165],0.313,[120,169,165,210,167,165,167,167,165],8.6,[120,172,165,190,167,165,167,167,165],[120,120,165,213,167,165,167,167,165],0.078,[120,177,165,194,167,165,167,167,165],[120,180,165,216,167,165,167,167,165],0.953,[120,183,165,218,167,165,167,167,165],0.952,[],[125],[222],"28-core Intel i7 CPU, no GPU; PlanarMesh uses all cores, baselines one core each (Sec. IV-A)",[],[225],"Ablation on Christ Church 03: number of scans for which seed planar-meshes are retained (0, 1, 10, All); All is the default setting; same metrics as Table I. Note: the text in Sec. IV-E attributes 0.295 s and 5.9 MB to 1-scan retention, whereas the table lists them for 0",{"slug":227,"group":228,"sourceId":5,"sourceLabel":6,"table":229,"selfRows":230,"metrics":231,"seqs":246,"entrants":254,"cells":266,"outcomes":468,"locators":470,"hardware":471,"wordings":472,"notes":473},"wang2025planarmesh-table-i","wang2025planarmesh:Table I","Table I",27,[232,234,236,239,241,242,243,244,245],{"label":233,"unit":130,"statistic":131,"alignment":40},"Per-Scan Time (s)",{"label":235,"unit":134,"statistic":131,"alignment":40},"File size (MB), PLY binary",{"label":237,"unit":238,"statistic":131,"alignment":40},"Num of Faces","count",{"label":240,"unit":238,"statistic":131,"alignment":40},"Num of Vertices",{"label":136,"unit":137,"statistic":138,"alignment":40},{"label":140,"unit":137,"statistic":141,"alignment":40},{"label":143,"unit":144,"statistic":131,"alignment":40},{"label":146,"unit":144,"statistic":131,"alignment":40},{"label":148,"unit":144,"statistic":131,"alignment":40},[247,250,252],{"dataset":80,"sequence":248,"environment":249},"Christ Church 03 (about 307 m)","existing buildings, indoor and outdoor (walking survey)",{"dataset":80,"sequence":251,"environment":249},"Keble College 03 (about 108 m)",{"dataset":80,"sequence":253,"environment":249},"Observatory 01 (about 324 m)",[255,258,261,263],{"name":256,"methodId":257,"linkable":156,"proposed":71,"self":71},"VDBFusion","vizzo2022vdbfusion",{"name":259,"methodId":260,"linkable":156,"proposed":71,"self":71},"ImMesh","lin2023immesh",{"name":262,"methodId":5,"linkable":156,"proposed":156,"self":156},"PlanarMesh (Ours)",{"name":264,"methodId":265,"linkable":156,"proposed":71,"self":71},"OctoMap","hornung2013octomap",[267,269,271,273,275,277,279,281,284,287,289,291,293,295,297,299,301,303,305,307,309,311,313,314,316,317,319,321,323,325,326,327,329,331,332,334,336,338,340,342,344,346,348,350,352,353,355,357,359,361,363,365,367,368,370,372,374,376,378,380,382,384,386,388,390,392,393,394,395,397,398,399,401,403,405,407,409,411,413,415,416,417,419,421,423,425,427,429,431,433,435,437,439,441,443,445,447,449,451,453,455,457,458,459,461,463,465,467],[165,165,165,268,167,165,165,167,165],0.871,[165,169,165,270,167,165,167,167,165],53.6,[165,172,165,272,167,165,167,167,165],1992391,[165,120,165,274,167,165,167,167,165],1152788,[165,177,165,276,167,165,167,167,165],0.044,[165,180,165,278,167,165,167,167,165],0.077,[165,183,165,280,167,165,167,167,165],0.918,[165,282,165,283,167,165,167,167,165],7,0.97,[165,285,165,286,167,165,167,167,165],8,0.943,[169,165,165,288,167,165,165,167,165],0.724,[169,169,165,290,167,165,167,167,165],370.9,[169,172,165,292,167,165,167,167,165],21180823,[169,120,165,294,167,165,167,167,165],7959789,[169,177,165,296,167,165,167,167,165],0.09,[169,180,165,298,167,165,167,167,165],0.186,[169,183,165,300,167,165,167,167,165],0.82,[169,282,165,302,167,165,167,167,165],0.99,[169,285,165,304,167,165,167,167,165],0.897,[172,165,165,306,167,165,165,167,165],0.392,[172,169,165,308,167,165,167,167,165],10.1,[172,172,165,310,167,165,167,167,165],398712,[172,120,165,312,167,165,167,167,165],411907,[172,177,165,173,167,165,167,167,165],[172,180,165,315,167,165,167,167,165],0.081,[172,183,165,194,167,165,167,167,165],[172,282,165,318,167,165,167,167,165],0.964,[172,285,165,320,167,165,167,167,165],0.957,[120,165,165,322,167,165,165,167,165],0.432,[120,169,165,324,167,165,167,167,165],3.4,[120,172,165,93,165,165,167,167,165],[120,120,165,93,165,165,167,167,165],[120,177,165,328,167,165,167,167,165],0.04,[120,180,165,330,167,165,167,167,165],0.083,[120,183,165,286,167,165,167,167,165],[120,282,165,333,167,165,167,167,165],0.991,[120,285,165,335,167,165,167,167,165],0.966,[165,165,169,337,167,165,165,167,165],0.968,[165,169,169,339,167,165,167,167,165],51.3,[165,172,169,341,167,165,167,167,165],1821087,[165,120,169,343,167,165,167,167,165],1150250,[165,177,169,345,167,165,167,167,165],0.033,[165,180,169,347,167,165,167,167,165],0.113,[165,183,169,349,167,165,167,167,165],0.962,[165,282,169,351,167,165,167,167,165],0.94,[165,285,169,194,167,165,167,167,165],[169,165,169,354,167,165,165,167,165],0.355,[169,169,169,356,167,165,167,167,165],163.8,[169,172,169,358,167,165,167,167,165],9057110,[169,120,169,360,167,165,167,167,165],3838040,[169,177,169,362,167,165,167,167,165],0.035,[169,180,169,364,167,165,167,167,165],0.064,[169,183,169,366,167,165,167,167,165],0.955,[169,282,169,280,167,165,167,167,165],[169,285,169,369,167,165,167,167,165],0.936,[172,165,169,371,167,165,165,167,165],0.416,[172,169,169,373,167,165,167,167,165],7.3,[172,172,169,375,167,165,167,167,165],287020,[172,120,169,377,167,165,167,167,165],296254,[172,177,169,379,167,165,167,167,165],0.031,[172,180,169,381,167,165,167,167,165],0.134,[172,183,169,383,167,165,167,167,165],0.979,[172,282,169,385,167,165,167,167,165],0.894,[172,285,169,387,167,165,167,167,165],0.935,[120,165,169,389,167,165,165,167,165],0.328,[120,169,169,391,167,165,167,167,165],24.3,[120,172,169,93,165,165,167,167,165],[120,120,169,93,165,165,167,167,165],[120,177,169,328,167,165,167,167,165],[120,180,169,396,167,165,167,167,165],0.159,[120,183,169,318,167,165,167,167,165],[120,282,169,335,167,165,167,167,165],[120,285,169,400,167,165,167,167,165],0.965,[165,165,172,402,167,165,165,167,165],2.406,[165,169,172,404,167,165,167,167,165],148.5,[165,172,172,406,167,165,167,167,165],5246193,[165,120,172,408,167,165,167,167,165],3346024,[165,177,172,410,167,165,167,167,165],0.047,[165,180,172,412,167,165,167,167,165],0.104,[165,183,172,414,167,165,167,167,165],0.899,[165,282,172,414,167,165,167,167,165],[165,285,172,414,167,165,167,167,165],[169,165,172,418,167,165,165,167,165],0.448,[169,169,172,420,167,165,167,167,165],424.7,[169,172,172,422,167,165,167,167,165],23448665,[169,120,172,424,167,165,167,167,165],9986250,[169,177,172,426,167,165,167,167,165],0.056,[169,180,172,428,167,165,167,167,165],0.089,[169,183,172,430,167,165,167,167,165],0.878,[169,282,172,432,167,165,167,167,165],0.832,[169,285,172,434,167,165,167,167,165],0.854,[172,165,172,436,167,165,165,167,165],0.213,[172,169,172,438,167,165,167,167,165],15.3,[172,172,172,440,167,165,167,167,165],546415,[172,120,172,442,167,165,167,167,165],682725,[172,177,172,444,167,165,167,167,165],0.042,[172,180,172,446,167,165,167,167,165],0.114,[172,183,172,448,167,165,167,167,165],0.929,[172,282,172,450,167,165,167,167,165],0.847,[172,285,172,452,167,165,167,167,165],0.886,[120,165,172,454,167,165,165,167,165],0.659,[120,169,172,456,167,165,167,167,165],67.8,[120,172,172,93,165,165,167,167,165],[120,120,172,93,165,165,167,167,165],[120,177,172,460,167,165,167,167,165],0.055,[120,180,172,462,167,165,167,167,165],0.15,[120,183,172,464,167,165,167,167,165],0.896,[120,282,172,466,167,165,167,167,165],0.941,[120,285,172,280,167,165,167,167,165],[469],"not_applicable (N\u002FA: occupancy map has no faces or vertices)",[229],[222],[],[474],"Oxford Spires; each method meshes individual scans with ground-truth poses (every undistorted scan registered to the TLS map); meshes sampled to the raw scan point count; distances to the TLS map after pre-filtering areas not seen by both; precision, recall and F-score at 0.1 m; OctoMap voxel 0.05 m, ImMesh and VDBFusion 0.1 m, baselines configured for about 1 Hz on one core, PlanarMesh on all 28 cores; file size as PLY binary; OctoMap has no faces or vertices (N\u002FA)",{"slug":476,"group":477,"sourceId":5,"sourceLabel":6,"table":478,"selfRows":172,"metrics":479,"seqs":485,"entrants":487,"cells":489,"outcomes":494,"locators":495,"hardware":497,"wordings":498,"notes":499},"wang2025planarmesh-text-sec-iv-d","wang2025planarmesh:Text Sec.IV-D","Text Sec.IV-D",[480,482],{"label":481,"unit":130,"statistic":138,"alignment":40},"average processing time per scan (about)",{"label":483,"unit":130,"statistic":484,"alignment":40},"peak processing time per scan","max",[486],{"dataset":80,"sequence":151,"environment":152},[488],{"name":7,"methodId":5,"linkable":156,"proposed":156,"self":156},[490,492],[165,165,165,491,167,165,165,167,165],0.4,[165,169,165,493,167,165,165,167,165],0.7,[],[496],"Sec. IV-D",[222],[],[500],"Per-scan processing time of PlanarMesh on Christ Church 03 (Fig. 7 stacked plot summarised in text); values stated in text, not read off the plot",[],1790510665558]