[{"data":1,"prerenderedAt":233},["ShallowReactive",2],{"method-zhou2018open3d":3},{"method":4,"reference":40,"equipment":58,"figures":59,"results":60},{"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":20,"limitations":25,"sensors":28,"platform":30,"estimator":31,"association":31,"timeModel":31,"deskew":31,"loopClosure":31,"globalOptimization":32,"mapRepresentation":33,"prior":34,"outputGeometry":35,"compute":36,"codeUrl":37,"codeLicense":38,"relatedVersions":39},"zhou2018open3d","Zhou et al., 2018","Open3D","Open3D: A Modern Library for 3D Data Processing",2018,"recent","C12","sensing_calibration_sync_preprocessing","Open3D 是提供 C++ 與 Python 介面的開源三維資料函式庫，核心資料結構為點雲、三角網格與 RGB-D 影像，內含體素降採樣、法向量估計、ICP 配準與體積整合（volumetric integration）等演算法，並以完整的 RGB-D 場景重建流程示範其功能；PUMA 即建構在此函式庫上。","An MIT-licensed C++\u002FPython library for point clouds, meshes and RGB-D images, including voxel downsampling, normal estimation, ICP and volumetric integration.","full_text_reviewed","preprint","supplementary","工具性函式庫，本論文未涉及營建。",[],[21,22,23,24],"Authors report their ICP is up to 25 times faster than PCL's counterpart (Sec. 4).","Clean code base with code review and continuous integration (Sec. 5).","OpenMP parallelisation sped up the most time-consuming functions by a factor of 3-6 on a modern CPU, and the reconstruction pipeline runs up to an order of magnitude faster than the original release by Choi et al. (Sec. 4).","The Python version of a load, downsample and normal-estimation task is about five times shorter than a PCL-based implementation (Sec. 2, Fig. 1).",[26,27],"Speed-up claims (25x ICP versus PCL, 3-6x from parallelisation) are given without hardware, datasets or timing protocol (Sec. 4).","No geometric accuracy evaluation; the paper is a library description (whole paper).",[29],"RGB-D",[],"not_applicable","multiway registration by pose graph optimization with in-house Gauss-Newton and Levenberg-Marquardt solvers; robust pose graph optimization inside the example RGB-D scene reconstruction pipeline (Sec. 2, 3.3, 3.4)","point clouds, triangle meshes, RGB-D images; volumetric (TSDF) integration module","none","point clouds and meshes (e.g., via volumetric integration)","CPU, OpenMP parallelization","https:\u002F\u002Fgithub.com\u002Fisl-org\u002FOpen3D","MIT (repository LICENSE)",[],{"id":5,"kind":41,"shortName":7,"title":8,"authors":42,"year":9,"venue":46,"venueType":16,"publisher":47,"volumeIssuePages":48,"doi":49,"arxivId":50,"url":51,"firstPublicDate":52,"publicationStatus":16,"metadataStatus":53,"fulltextStatus":15,"era":10,"classicReason":31,"codeUrl":37,"cluster":11,"topics":54,"mdpi":55,"verification":56,"label":6,"fulltextRoute":47,"versionRead":57,"addedByCensus":55},"software",[43,44,45],"Qian-Yi Zhou","Jaesik Park","Vladlen Koltun","arXiv preprint","arXiv","not_reported",null,"1801.09847","https:\u002F\u002Farxiv.org\u002Fabs\u002F1801.09847","2018-01-30","metadata_verified",[11],false,"confirmed","arXiv 1801.09847v1 (6 pp.; only version; arXiv non-exclusive distribution licence)",[],[],{"totalRows":61,"groupCount":62,"groups":63,"others":232},15,2,[64,196],{"slug":65,"group":66,"sourceId":67,"sourceLabel":68,"table":69,"selfRows":70,"metrics":71,"seqs":82,"entrants":91,"cells":102,"outcomes":189,"locators":190,"hardware":192,"wordings":193,"notes":194},"elasticity-ct2022-table-vi","elasticity_ct2022:Table VI","elasticity_ct2022","Park et al., 2022","Table VI",12,[72,76,78,81],{"label":73,"unit":74,"statistic":75,"alignment":31},"e_t translation error","m","RMSE",{"label":73,"unit":74,"statistic":77,"alignment":31},"std",{"label":79,"unit":80,"statistic":75,"alignment":31},"e_r rotation error (rotation vector norm)","rad",{"label":79,"unit":80,"statistic":77,"alignment":31},[83,87,89],{"dataset":84,"sequence":85,"environment":86},"authors' mixed indoor and outdoor point clouds","Easy initial guess","indoor and outdoor mixed",{"dataset":84,"sequence":88,"environment":86},"Medium initial guess",{"dataset":84,"sequence":90,"environment":86},"Hard initial guess",[92,96,98,100],{"name":93,"methodId":94,"linkable":95,"proposed":55,"self":55},"(a) Sparse surfel ICP (configuration of previous work [2])","elasticlidarfusion2018",true,{"name":97,"methodId":5,"linkable":95,"proposed":55,"self":95},"(b) Open3D global registration (FPFH + RANSAC) [60]",{"name":99,"methodId":49,"linkable":55,"proposed":55,"self":55},"(c) SHOT initialisation + point-to-plane ICP [61]",{"name":101,"methodId":67,"linkable":95,"proposed":95,"self":55},"(d) Proposed sequential metric localisation",[103,107,109,111,113,115,117,119,121,123,125,127,129,130,131,133,135,137,139,141,143,145,147,148,150,152,154,156,158,159,161,163,164,166,168,170,171,173,175,176,178,180,182,183,185,186,187,188],[104,104,104,105,106,104,106,106,104],0,0.04,-1,[104,108,104,105,106,104,106,106,104],1,[104,62,104,110,106,104,106,106,104],0.01,[104,112,104,110,106,104,106,106,104],3,[108,104,104,114,106,104,106,106,104],0.3,[108,108,104,116,106,104,106,106,104],0.65,[108,62,104,118,106,104,106,106,104],0.03,[108,112,104,120,106,104,106,106,104],0.06,[62,104,104,122,106,104,106,106,104],1.49,[62,108,104,124,106,104,106,106,104],1.52,[62,62,104,126,106,104,106,106,104],0.07,[62,112,104,128,106,104,106,106,104],0.12,[112,104,104,118,106,104,106,106,104],[112,108,104,110,106,104,106,106,104],[112,62,104,132,106,104,106,106,104],0.001,[112,112,104,134,106,104,106,106,104],0.0005,[104,104,108,136,106,104,106,106,104],0.4,[104,108,108,138,106,104,106,106,104],0.51,[104,62,108,140,106,104,106,106,104],0.19,[104,112,108,142,106,104,106,106,104],0.31,[108,104,108,144,106,104,106,106,104],1.64,[108,108,108,146,106,104,106,106,104],2.39,[108,62,108,114,106,104,106,106,104],[108,112,108,149,106,104,106,106,104],0.38,[62,104,108,151,106,104,106,106,104],1.53,[62,108,108,153,106,104,106,106,104],1.55,[62,62,108,155,106,104,106,106,104],0.11,[62,112,108,157,106,104,106,106,104],0.29,[112,104,108,105,106,104,106,106,104],[112,108,108,160,106,104,106,106,104],0.02,[112,62,108,162,106,104,106,106,104],0.004,[112,112,108,132,106,104,106,106,104],[104,104,62,165,106,104,106,106,104],2.52,[104,108,62,167,106,104,106,106,104],0.87,[104,62,62,169,106,104,106,106,104],2.42,[104,112,62,153,106,104,106,106,104],[108,104,62,172,106,104,106,106,104],13.8,[108,108,62,174,106,104,106,106,104],22.4,[108,62,62,149,106,104,106,106,104],[108,112,62,177,106,104,106,106,104],0.63,[62,104,62,179,106,104,106,106,104],7.59,[62,108,62,181,106,104,106,106,104],18.56,[62,62,62,114,106,104,106,106,104],[62,112,62,184,106,104,106,106,104],0.71,[112,104,62,120,106,104,106,106,104],[112,108,62,105,106,104,106,106,104],[112,62,62,162,106,104,106,106,104],[112,112,62,132,106,104,106,106,104],[],[191],"Table VI (VoR, p. 993; identical to arXiv v1 Table V)",[],[],[195],"loop-closure misalignment estimation on mixed indoor and outdoor data; ground truth from the globally optimised trajectory; 10 locations x 50 random initial guesses (500 triggers) per level; Easy sigma_theta_z 10 deg, sigma_theta_xy 1 deg, sigma_t 0.5 m; Medium 50, 5, 5; Hard 100, 20, 50; text calls the values RMSE, caption calls them error norms with std in parentheses. Values read from the VoR Table VI by the second checker",{"slug":197,"group":198,"sourceId":5,"sourceLabel":6,"table":199,"selfRows":112,"metrics":200,"seqs":209,"entrants":211,"cells":218,"outcomes":223,"locators":226,"hardware":228,"wordings":229,"notes":230},"zhou2018open3d-text-sec-4","zhou2018open3d:Text Sec.4","Text Sec.4",[201,205,207],{"label":202,"unit":203,"statistic":204,"alignment":34},"ICP speed-up relative to the PCL counterpart (up to)","x","max",{"label":206,"unit":203,"statistic":204,"alignment":34},"reconstruction pipeline speed-up relative to the original Choi et al. release (up to)",{"label":208,"unit":203,"statistic":48,"alignment":34},"speed-up of the most time-consuming functions from OpenMP parallelisation",[210],{"dataset":48,"sequence":48,"environment":48},[212,214,216],{"name":213,"methodId":5,"linkable":95,"proposed":95,"self":95},"Open3D ICP versus PCL ICP",{"name":215,"methodId":5,"linkable":95,"proposed":95,"self":95},"Open3D implementation of the Choi et al. reconstruction pipeline",{"name":217,"methodId":5,"linkable":95,"proposed":95,"self":95},"Open3D backend with OpenMP parallelisation",[219,221,222],[104,104,104,220,106,104,106,106,104],25,[108,108,104,49,104,104,106,106,104],[62,62,104,49,108,104,106,106,104],[224,225],"other: 'up to an order of magnitude faster'; no exact number given","other: stated as a factor of 3-6 on a modern CPU",[227],"Sec. 4",[],[],[231],"Speed claims stated in the Optimization section without hardware, datasets or timing protocol",[],1790510665515]