[{"data":1,"prerenderedAt":78},["ShallowReactive",2],{"method-yun2018reflection":3},{"method":4,"reference":45,"equipment":63,"figures":77,"results":42},{"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":21,"limitations":25,"sensors":31,"platform":33,"estimator":35,"association":36,"timeModel":37,"deskew":37,"loopClosure":37,"globalOptimization":38,"mapRepresentation":37,"prior":39,"outputGeometry":40,"compute":41,"codeUrl":42,"codeLicense":43,"relatedVersions":44},"yun2018reflection","Yun & Sim, 2018","Glass reflection removal","Reflection Removal for Large-Scale 3D Point Clouds",2018,"recent","C13","sensing_calibration_sync_preprocessing","地面雷射掃描遇到玻璃時，同一雷射脈衝可能同時產生玻璃點、穿透點，以及經玻璃反射而落在玻璃後方的虛像點。本法利用 RIEGL VZ-400 的多回波特性，把單位球面切成約 3×3 個脈衝的面片並計算投影點數，以兩成分高斯混合模型與 EM 分出一般與玻璃面片，再以 RANSAC 擬合最靠近掃描儀的主要玻璃平面，並依距離計算可靠度。玻璃平面後方的點若經 Householder 鏡射後能找到位置相近且 FPFH 特徵相似的真實點，就判為虛像點，最後以資料項加鄰點平滑項組成的標記能量函數，用 ICM 求解後移除虛像點。以六個含玻璃的戶外場景點雲作定性驗證，每個模型處理時間約 55 至 135 s。","Removes glass-reflection ghost points from single TLS scans: echo counts per sphere patch of about 3x3 pulses are classified by a two-component GMM, the dominant glass plane is fitted by RANSAC and weighted by distance-based reliability, and points behind it are labelled virtual when a Householder-mirrored real counterpart with similar FPFH exists, refined by a labelling energy with data and neighbour smoothness terms solved with ICM; validated qualitatively on six outdoor RIEGL VZ-400 scans.","full_text_reviewed","peer_reviewed_published","supplementary","not_reported。作者以建物玻璃帷幕與車輛玻璃造成的反射虛像為動機（Sec. 1, Sec. 2），測試對象為六個戶外建物與庭園場景，未涉及施工工地，也沒有定量評估。玻璃帷幕與窗戶在既有建物與完工階段常見，虛像點可能干擾竣工比對與 Scan-to-BIM（推論）。",[20],"completed_building",[22,23,24],"Faithful glass-region estimation and removal of virtual points on outdoor TLS scenes (abstract, Sec. 5, Fig. 9)","Presented by the authors as the first reflection-removal algorithm for large-scale 3D point clouds (Sec. 1)","Uses geometry and echo counts only, so no camera is needed (Sec. 7)",[26,27,28,29,30],"Assumes a single dominant glass plane per scan (Sec. 7)","No quantitative evaluation because reflection-free ground truth is hard to obtain (Sec. 7)","Virtual points remain when their real counterparts are occluded or glass patches get low reliability, and real trees crossing the extended glass plane can be removed (Sec. 5)","β1 and β2 are set empirically per model (Sec. 5)","Tested on static TLS outdoor scenes; applicability to mobile SLAM scans not shown (inference)",[32],"RIEGL VZ-400 terrestrial laser scanner with multiple echo returns, angular resolution 0.06° x 0.06°",[34],"static TLS","two-component Gaussian mixture (EM) on per-patch point counts; RANSAC glass-plane fit with distance-weighted reliability; per-point score from reflection-symmetry distance and FPFH Hellinger similarity; binary labelling by an energy with data and neighbour smoothness terms (MRF-type; the paper does not use the term) minimised with ICM","k-d tree nearest real point to the Householder-mirrored position of each candidate behind the glass plane; FPFH on 50 nearest neighbours; 48-neighbour smoothness term limited to 0.1% of the bounding-box diagonal","not_applicable","none","multi-echo information","point cloud with virtual reflection points removed","Intel i7-4790K (4.38 GHz as written); 54.7 to 134.8 s per model of 4.9 to 9.7 million points, with descriptor computation taking more than half of the total time in most cases (5 of 6 models in Table 1)",null,"not_verified",[],{"id":5,"kind":46,"shortName":7,"title":8,"authors":47,"year":9,"venue":50,"venueType":51,"publisher":52,"volumeIssuePages":53,"doi":54,"arxivId":42,"url":55,"firstPublicDate":56,"publicationStatus":16,"metadataStatus":57,"fulltextStatus":15,"era":10,"classicReason":37,"codeUrl":42,"cluster":11,"topics":58,"mdpi":59,"verification":60,"label":6,"fulltextRoute":61,"versionRead":62,"addedByCensus":59},"component",[48,49],"Jae-Seong Yun","Jae-Young Sim","2018 IEEE\u002FCVF Conference on Computer Vision and Pattern Recognition","conference","IEEE","pp. 4597-4605","10.1109\u002Fcvpr.2018.00483","https:\u002F\u002Fdoi.org\u002F10.1109\u002FCVPR.2018.00483","2018-06","metadata_verified",[11],false,"confirmed","other","CVF Open Access copy of the CVPR 2018 paper (pages 4597-4605, same pagination as the IEEE proceedings); IEEE Xplore version of record not compared",[64,71],{"category":65,"model":66,"canonical":67,"role":68,"dataset":42,"specs":69,"locator":70},"tls_scanner","RIEGL VZ-400","Riegl VZ-400","method input","multiple echo returns; angular resolution 0.06° azimuthal and 0.06° polar; about 5 to 6 million points per model","Sec. 2, 5",{"category":72,"model":73,"canonical":73,"role":74,"dataset":42,"specs":75,"locator":76},"compute","Intel i7-4790k","compute for runtime","4.38 GHz as written","Sec. 5; Table 1",[],1790510663639]