[{"data":1,"prerenderedAt":101},["ShallowReactive",2],{"method-stereoscan2011":3},{"method":4,"reference":58,"equipment":80,"figures":100,"results":69},{"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":33,"platform":35,"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},"stereoscan2011","Geiger et al., 2011","StereoScan (LIBVISO2)","StereoScan: Dense 3d reconstruction in real-time",2011,"classic","C08","odometry_with_local_mapping","StereoScan 以立體影像在單一 CPU 上即時建立三維地圖。前端以斑點與角點遮罩偵測特徵，用 Sobel 響應的稀疏 SAD 比對，並要求左右影像與前後影格四張影像形成環狀匹配；再以 RANSAC 包覆的高斯牛頓法最小化左右影像重投影誤差估計自我運動，並用等加速度卡爾曼濾波平滑速度。另一執行緒以 ELAS 計算稠密視差，依估計位姿把三維點重投影到目前影格，貪婪地合併並平均重複點，產生一致的點雲。","StereoScan (LIBVISO2) combines fast circular sparse feature matching and RANSAC reprojection-error stereo odometry at 25 fps with ELAS dense stereo at 3 to 4 fps and a greedy reprojection-based point fusion to build consistent 3D point clouds in real time on a CPU.","full_text_reviewed","peer_reviewed_published","background","論文未在營建工地測試；資料為 Karlsruhe 市區戶外立體影像序列，以 OXTS GPS\u002FIMU 作弱真值，重建品質只做定性展示。它說明只用相機也能即時產生具公制尺度的融合點雲，對低成本工地巡檢或車載掃描有參考價值，但缺乏迴圈閉合與點雲精度量化，不能直接視為工程量測（推論）。",[20,21],"public_benchmark","independent_reference",[23,24,25,26,27],"Visual odometry takes 4.3 ms versus about one second for Kitt et al. (2010) on 200 matches, a speed-up of more than 200 (Sec. IV-B; Fig. 6b)","Multi-stage matching lowers running time while increasing the number of matches (Sec. IV-A)","Localisation accuracy comparable to Kitt et al. on the Karlsruhe sequences despite much faster runtime (Sec. IV-B; Fig. 7, plotted only)","First system reported by the authors to process about one-megapixel stereo images online on a single CPU (Sec. I)","Greedy fusion reduces stored points and averages measurement noise over frames (Sec. III-D)",[29,30,31,32],"No loop closure or global optimisation; sequences are short and the soft reset of outdated points is not handled (Sec. III-D)","Reconstruction targets static scenes; handling dynamic objects is future work (Secs. III, V)","GPS\u002FIMU reference is weak ground truth with errors up to about two metres in inner-city areas, so accuracy is judged only qualitatively in plots (Sec. IV-B)","Three-dimensional reconstruction is evaluated only qualitatively (Sec. IV-C)",[34],"stereo camera (calibrated, rectified)",[36,37,38],"mobile platform recording the Karlsruhe stereo sequences (inner-city scenes","sequence names '2009_09_08_drive_00xx'","the vehicle is not described in the paper)","frame-to-frame stereo egomotion: Gauss-Newton minimisation of left and right reprojection errors of triangulated features inside RANSAC (50 iterations of 3-point samples), refinement on all inliers, then a constant-acceleration Kalman filter on the velocity (Sec. III-B)","blob and corner features from 5 x 5 masks with non-maximum and non-minimum suppression; SAD of quantised Sobel responses at 16 sparse locations of an 11 x 11 window; circular matching over left and right images of two frames with 1 pixel epipolar tolerance; Delaunay-neighbourhood support filtering; two-pass search narrowed per 50 x 50 pixel bin; bucketing to 200 to 500 features (Secs. III-A, III-B)","discrete stereo frames (10 fps in the Karlsruhe data)","not_applicable","none","point-based 3D model: ELAS disparity maps converted to 3D and greedily fused by reprojecting previous points into the current image and averaging points that fall on valid disparities (Sec. III-D)","calibrated stereo rig with rectified images (Sec. III)","visual odometry trajectory at 25 fps and a fused dense 3D point cloud updated from new depth maps at 3 to 4 fps (abstract; Sec. III)","two CPU threads: feature matching 36.6 ms plus visual odometry 4.3 ms per frame (about 25 fps) in the online setting; ELAS dense stereo 3 to 4 fps on a single i7 core at 3.0 GHz for about 0.5 MP images; appending one disparity map to the model usually under 50 ms (Fig. 6; Secs. III-C, III-D)","https:\u002F\u002Fwww.cvlibs.net\u002Fsoftware\u002Flibviso\u002F","GNU GPL (LIBVISO page: code published under the GNU General Public License; version not stated)",[51,54],{"relation":52,"title":53,"doi_or_url":48},"code_release","LIBVISO2 (C++ library with MATLAB wrappers; the cvlibs.net page gives this IV 2011 paper as the LIBVISO2 citation); dense stereo from LIBELAS",{"relation":55,"title":56,"doi_or_url":57},"predecessor_method","Visual odometry based on stereo image sequences with RANSAC-based outlier rejection scheme (Kitt, Geiger, Lategahn, IV 2010; the freely available visual odometry library used as the baseline, cited as [16])","10.1109\u002FIVS.2010.5548123",{"id":5,"kind":59,"shortName":7,"title":8,"authors":60,"year":9,"venue":64,"venueType":65,"publisher":66,"volumeIssuePages":67,"doi":68,"arxivId":69,"url":70,"firstPublicDate":71,"publicationStatus":16,"metadataStatus":72,"fulltextStatus":15,"era":10,"classicReason":73,"codeUrl":48,"cluster":11,"topics":74,"mdpi":75,"verification":76,"label":6,"fulltextRoute":77,"versionRead":78,"addedByCensus":79},"method",[61,62,63],"Andreas Geiger","Julius Ziegler","Christoph Stiller","2011 IEEE Intelligent Vehicles Symposium (IV)","conference","IEEE","pp. 963-968","10.1109\u002Fivs.2011.5940405",null,"https:\u002F\u002Fwww.cvlibs.net\u002Fpublications\u002FGeiger2011IV.pdf","2011-06","metadata_verified","reproducible baseline and principle reused: LIBVISO2 stereo visual odometry combined with ELAS dense stereo and greedy multi-frame point fusion, a classic camera-only route to metric 3D point clouds on a CPU.",[11],false,"corrected","author copy","author PDF from cvlibs.net (created 2011-08-03); IEEE version of record not read",true,[81,88,94],{"category":82,"model":83,"canonical":83,"role":84,"dataset":85,"specs":86,"locator":87},"stereo_camera","stereo camera of the Karlsruhe dataset (model not reported)","dataset sensor","Karlsruhe dataset (cvlibs.net)","1344 x 391 pixels, 10 fps, calibrated and rectified","Sec. IV",{"category":89,"model":90,"canonical":90,"role":91,"dataset":85,"specs":92,"locator":93},"gnss","OXTS RT 3003 GPS\u002FIMU","reference or ground truth","'weak' ground truth; errors up to two metres possible in inner-city scenarios","Sec. IV-B",{"category":95,"model":96,"canonical":96,"role":97,"dataset":69,"specs":98,"locator":99},"compute","i7 CPU","compute for runtime","single core at 3.0 GHz used for ELAS dense stereo; the pipeline assumes two CPU cores","Secs. III, III-C",[],1790510656119]