[{"data":1,"prerenderedAt":879},["ShallowReactive",2],{"method-livgaussmap2024":3},{"method":4,"reference":55,"equipment":76,"figures":139,"results":140},{"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":34,"platform":38,"estimator":39,"association":40,"timeModel":41,"deskew":42,"loopClosure":43,"globalOptimization":43,"mapRepresentation":44,"prior":45,"outputGeometry":46,"compute":47,"codeUrl":48,"codeLicense":49,"relatedVersions":50},"livgaussmap2024","Hong et al., 2024","LIV-GaussMap","LIV-GaussMap: LiDAR-Inertial-Visual Fusion for Real-Time 3D Radiance Field Map Rendering",2024,"recent","C09","map_representation_or_reconstruction","LIV-GaussMap 以硬體同步的 LiDAR-慣性系統及尺寸自適應體素取得位姿與平面結構，將體素平面的共變異轉為高斯初始形狀，再用影像光度梯度精修球諧顏色與結構。作者在 FusionPortable 以 Chamfer、EMD 與 F-score 評估結構，並報告光度最佳化會使結構品質略為下降，顯示渲染與幾何之間的取捨。","Initializes surface Gaussians from LiDAR-inertial adaptive-voxel planes and refines them photometrically; photometric refinement slightly degrades structure metrics.","full_text_reviewed","peer_reviewed_published","main_body","論文未在營建場域測試；資料為 FusionPortable、FAST-LIVO 及作者自錄室內外資料；作者提及數位孿生潛力但未驗證工程任務。",[20,21],"public_benchmark","controlled_experiment",[23,24,25,26,27],"LiDAR-based initialization clearly improves CD, EMD and F-score over purely visual approaches (Sec. IV-C)","Supports solid-state and mechanical LiDARs (abstract)","Best interpolation PSNR (32.787) and extrapolation PSNR (19.220) in Table II; extrapolation PSNR 4.1 dB above 3D-GS (15.111) (Table II)","On FusionPortable, full-model CD 0.107 and EMD 0.435 vs 0.149 and 0.698 for 3D-GS (Table IV)","Full model (Case IV) exceeds 3D-GS interpolation and extrapolation PSNR on the average of five sequences (30.877 vs 30.168; 22.403 vs 21.185) (Table III)",[29,30,31,32,33],"Photometric optimization of Gaussian structure slightly reduced structural quality; pose refinement had mixed effects on F-score (Sec. IV-C)","Training and rendering slower than 3DGS due to dense LiDAR points: 14m25s and 43 FPS vs 8m11s and 131 FPS (Sec. V, Table II)","LiDAR structure is unreliable on glass and in over- or under-scanned areas, requiring photometric densification and pruning (Sec. III-C)","LiDAR initialization without visual structure optimization (Case II) lowered interpolation PSNR relative to 3D-GS on four of five sequences (Sec. IV-B, Table III)","F-score is highest for Case II (0.807) rather than the full model (0.751); CD and EMD units are not stated (Table IV)",[35,36,37],"3D LiDAR (Livox Avia; Ouster OS1-128; solid-state RealSense L515)","IMU","monocular camera (global shutter; rolling shutter on the L515)",[],"LiDAR-inertial odometry with size-adaptive voxel map provides poses; Gaussians refined by photometric gradients","LiDAR plane covariances for Gaussian initialization; photometric gradients for refinement","discrete poses","not_applicable","none reported","surface Gaussians initialized from size-adaptive voxel plane covariances; spherical-harmonic colour","none","Gaussian map with renderings; structure evaluated against ground-truth point clouds (CD, EMD, F-score)","Intel i9-12900K + RTX 4090 (Sec. IV)","https:\u002F\u002Fgithub.com\u002Fsheng00125\u002FLIV-GaussMap","none (repository contains only README.md and figures; no source code or LICENSE at check time)",[51],{"relation":52,"title":53,"doi_or_url":54},"preprint","arXiv:2401.14857","https:\u002F\u002Farxiv.org\u002Fabs\u002F2401.14857",{"id":5,"kind":56,"shortName":7,"title":8,"authors":57,"year":9,"venue":62,"venueType":63,"publisher":64,"volumeIssuePages":65,"doi":66,"arxivId":67,"url":68,"firstPublicDate":69,"publicationStatus":16,"metadataStatus":70,"fulltextStatus":15,"era":10,"classicReason":42,"codeUrl":48,"cluster":11,"topics":71,"mdpi":72,"verification":73,"label":6,"fulltextRoute":74,"versionRead":75,"addedByCensus":72},"method",[58,59,60,61],"Sheng Hong","Junjie He","Xinhu Zheng","Chunran Zheng","IEEE Robotics and Automation Letters","journal","IEEE","9(11), 9765-9772","10.1109\u002Flra.2024.3400149","2401.14857","https:\u002F\u002Fapi.crossref.org\u002Fworks\u002F10.1109\u002Flra.2024.3400149","2024-01-26","metadata_verified",[11],false,"corrected","arXiv","arXiv v2 HTML (2024-05-17), accepted manuscript (received 2024-01-17, revised 2024-04-01, accepted 2024-04-15); IEEE RA-L version of record not opened",[77,84,89,93,97,99,102,109,112,115,118,121,123,130,136],{"category":78,"model":79,"canonical":79,"role":80,"dataset":81,"specs":82,"locator":83},"lidar","Livox Avia","dataset sensor","FAST-LIVO dataset","240,000 points\u002Fs; mechanical, non-repetitive; 3 m to 450 m; FoV 70.4 x 77.2 deg","Table I",{"category":85,"model":86,"canonical":87,"role":80,"dataset":81,"specs":88,"locator":83},"imu","BM1088 (as written)","BM1088","not_reported",{"category":90,"model":91,"canonical":91,"role":80,"dataset":81,"specs":92,"locator":83},"camera","MV-CA013-21UC","global shutter, 1280 x 1024, FoV 72 x 60 deg, hardware-synchronized",{"category":78,"model":94,"canonical":94,"role":80,"dataset":95,"specs":96,"locator":83},"Ouster OS1-128","FusionPortable","2,621,440 points\u002Fs; mechanical, repetitive; 1 m to 120 m; FoV 45 x 360 deg",{"category":85,"model":98,"canonical":98,"role":80,"dataset":95,"specs":88,"locator":83},"ICM20948",{"category":90,"model":100,"canonical":100,"role":80,"dataset":95,"specs":101,"locator":83},"BFS-U3-31S4C (written 'FILR BFS-U3-31S4C')","global shutter, 1024 x 768, FoV 66.5 x 82.9 deg",{"category":78,"model":103,"canonical":104,"role":105,"dataset":106,"specs":107,"locator":108},"RealSense L515","Intel RealSense L515","method input",null,"solid-state; 23,000,000 points\u002Fs and 9 m to 25 m as written; FoV 70 x 55 deg; indoor only","Table I (Our Device I); Sec. 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III",{"category":85,"model":119,"canonical":119,"role":105,"dataset":106,"specs":88,"locator":120},"BMI088","Table I (Our Device II)",{"category":90,"model":91,"canonical":91,"role":105,"dataset":106,"specs":122,"locator":120},"global shutter, 1280 x 1024, FoV 72 x 60 deg",{"category":124,"model":125,"canonical":125,"role":126,"dataset":127,"specs":128,"locator":129},"other","ground-truth structure point clouds","reference or ground truth","FusionPortable; self-collected","provided for FusionPortable and both self-collected devices (acquisition instrument not stated)","Table I; Sec. IV",{"category":131,"model":132,"canonical":132,"role":133,"dataset":106,"specs":134,"locator":135},"compute","Intel Core i9 12900K","compute for runtime","3.50 GHz","Sec. IV",{"category":131,"model":137,"canonical":137,"role":133,"dataset":106,"specs":138,"locator":135},"NVIDIA GeForce RTX 4090","single GPU",[],{"totalRows":141,"groupCount":142,"groups":143,"others":878},77,4,[144,496,669,827],{"slug":145,"group":146,"sourceId":147,"sourceLabel":148,"table":83,"selfRows":149,"metrics":150,"seqs":161,"entrants":190,"cells":197,"outcomes":490,"locators":491,"hardware":492,"wordings":493,"notes":494},"litgs2026-table-i","litgs2026:Table I","litgs2026","Shi et al., 2026",48,[151,154,157,159],{"label":152,"unit":153,"statistic":88,"alignment":45},"PSNR (rendering fidelity, higher is better)","dB (unit not printed)",{"label":155,"unit":156,"statistic":88,"alignment":45},"SSIM (higher is better)","unitless",{"label":158,"unit":156,"statistic":88,"alignment":45},"LPIPS (lower is better)",{"label":160,"unit":88,"statistic":88,"alignment":88},"Earth Mover's Distance between reconstructed and reference point clouds (geometric consistency, lower is better)",[162,166,168,170,172,174,176,180,182,184,186,188],{"dataset":163,"sequence":164,"environment":165},"authors' private thermal dataset","Car","outdoor private scenes under varied lighting",{"dataset":163,"sequence":167,"environment":165},"Logo",{"dataset":163,"sequence":169,"environment":165},"Sundial",{"dataset":163,"sequence":171,"environment":165},"Tree-stump",{"dataset":163,"sequence":173,"environment":165},"Bushes",{"dataset":163,"sequence":175,"environment":165},"Avg. 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Gaussian-splatting reconstruction quality; private scenes captured with the authors' Livox Avia + thermal rig at different times of day (Fig. 3 caption lists 12:00 p.m. to 6:00 a.m.), public scenes are five M2DGR sequences; EMD reference point cloud source not described; Avg. columns printed by the authors",{"slug":497,"group":498,"sourceId":5,"sourceLabel":6,"table":499,"selfRows":500,"metrics":501,"seqs":523,"entrants":543,"cells":553,"outcomes":662,"locators":663,"hardware":664,"wordings":666,"notes":667},"livgaussmap2024-table-iii","livgaussmap2024:Table III","Table III",18,[502,505,507,509,510,513,515,517,519,521],{"label":503,"unit":504,"statistic":88,"alignment":45},"PSNR [dB] (Interpolated)","dB",{"label":506,"unit":504,"statistic":88,"alignment":45},"PSNR [dB] (Extrapolate)",{"label":503,"unit":504,"statistic":508,"alignment":45},"mean",{"label":506,"unit":504,"statistic":508,"alignment":45},{"label":511,"unit":512,"statistic":88,"alignment":45},"Cost time (written 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Core i9 12900K 3.50 GHz, single NVIDIA GeForce RTX 4090",[],[668],"Ablation of map structure optimization: Case I = 3D-GS baseline; Case II = LiDAR-initialized Gaussians without visual structure optimization; Case III = Case II plus photometric position optimization; Case IV = full method with Gaussian pose refinement; SSIM and LPIPS rows of this table omitted here; Cases II and III are ablation variants (method_id null)",{"slug":670,"group":671,"sourceId":5,"sourceLabel":6,"table":672,"selfRows":223,"metrics":673,"seqs":691,"entrants":695,"cells":712,"outcomes":821,"locators":822,"hardware":823,"wordings":824,"notes":825},"livgaussmap2024-table-ii","livgaussmap2024:Table II","Table II",[674,676,678,680,682,684,686,689],{"label":675,"unit":504,"statistic":88,"alignment":45},"PSNR (Interpolate)",{"label":677,"unit":45,"statistic":88,"alignment":45},"SSIM (Interpolate)",{"label":679,"unit":45,"statistic":88,"alignment":45},"LPIPS 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[16]",{"name":707,"methodId":106,"linkable":72,"proposed":72,"self":72},"F2-NeRF [25]",{"name":709,"methodId":546,"linkable":193,"proposed":72,"self":72},"3D-GS [1]",{"name":711,"methodId":5,"linkable":193,"proposed":193,"self":193},"Our 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synthesis on interpolated and extrapolated views on a real-world dataset (dataset and sequence not named for this table); asterisk methods were enhanced with dense LiDAR point clouds; cost time and FPS on the authors' desktop",{"slug":828,"group":829,"sourceId":5,"sourceLabel":6,"table":830,"selfRows":209,"metrics":831,"seqs":838,"entrants":842,"cells":847,"outcomes":872,"locators":873,"hardware":874,"wordings":875,"notes":876},"livgaussmap2024-table-iv","livgaussmap2024:Table IV","Table IV",[832,834,836],{"label":833,"unit":88,"statistic":88,"alignment":88},"CD (Chamfer Discrepancy [26])",{"label":835,"unit":88,"statistic":88,"alignment":88},"EMD (Earth Mover Distance [27])",{"label":837,"unit":45,"statistic":88,"alignment":88},"F-score [28]",[839],{"dataset":95,"sequence":840,"environment":841},"HKUST_indoor (per Table I)","indoor campus building, Ouster OS1-128",[843,844,845,846],{"name":545,"methodId":546,"linkable":193,"proposed":72,"self":72},{"name":548,"methodId":106,"linkable":72,"proposed":72,"self":72},{"name":550,"methodId":106,"linkable":72,"proposed":72,"self":72},{"name":552,"methodId":5,"linkable":193,"proposed":193,"self":193},[848,850,852,854,856,858,860,862,864,866,868,870],[199,199,199,849,201,199,201,201,199],0.149,[199,203,199,851,201,199,201,201,199],0.698,[199,206,199,853,201,199,201,201,199],0.544,[203,199,199,855,201,199,201,201,199],0.114,[203,203,199,857,201,199,201,201,199],0.553,[203,206,199,859,201,199,201,201,199],0.807,[206,199,199,861,201,199,201,201,199],0.109,[206,203,199,863,201,199,201,201,199],0.614,[206,206,199,865,201,199,201,201,199],0.682,[209,199,199,867,201,199,201,201,199],0.107,[209,203,199,869,201,199,201,201,199],0.435,[209,206,199,871,201,199,201,201,199],0.751,[],[830],[],[],[877],"Structure accuracy of the Gaussian map against the ground-truth point cloud on FusionPortable (sequence HKUST_indoor per Table I); CD and EMD units not stated",[],1790510655571]