基于图像大规模场景三维重建

无人维环境的感知无人维环境的感知场景的三维结构和相机的六自由度空间位姿途径途径一：SimultaneousLocalizationandMapping—视频—重建场景稀疏/准稠密/稠密结构与相机位姿（on-—闭环检测+图优化（on-途径二：StructurefromMotion—多视—重建场景稀疏结构与相机位姿（off-—通过PnP计算相机实时位姿（on-SLAMSfMLocalMatchingSLAMSfMLocalMatchingLoopClosurePoseGraphOptimizationFullBundleAdjustmentFullMatchingSeedTwoViewReconstructionLocalBundleAdjustmentFullBundleAdjustment••••••••••••PoseGraphViewGraph简化TracksRecentPublications:ECCV2018、3DV2018、CVPR2017、3DVISPRSP&RS2018、PR2018、PRProgressiveSfMwithProgressiveSfMwithOrthogonal•HainanCui,ShuhanShen,WeiGao,ZhihengWang.ProgressiveLarge-ScaleStructure-from-MotionwithOrthogonalMSTs.3DV2018HainanCui,XiangGao,ShuhanShen,ZhanyiHu.HSfM:HybridStructure-from-Motion.CVPR2017(Spotlight)HainanCui,ShuhanShen,XiangGao,ZhanyiHu.BatchedIncrementalfrom-Motion.3DVHainanCui,ShuhanShen,ZhanyiHu.TracksSelectionforRobust,EfficientandScalableLarge-ScaleStructurefromMotion.PatternRecognition,2017•••ProgressiveSfMwithOrthogonalKeyProgressiveSfMwithOrthogonalKeyProblemsin•••EverythingcomesfromimagematchingImagematchingistime-consumingBasic•Similarity(Edge:matchedPose(Edge:relativeView(Edge:relativegeometryinlier•••ProgressiveSfMwithOrthogonalProgressiveProgressiveSfMwithOrthogonalProgressiveSceneSystemMain•••••••Similaritygraphconstruction(withBoW)OrthogonalMSTsselectionImagematchinginMSTTracksselectionRotationaveragingIncrementalSfMFullBundleProgressiveSfMwithOrthogonalProgressiveSfMwithOrthogonalOrthogonalMaximumSpanningTrees,(3)1st-MST+2nd-(1)Similarity(2)1st-ProgressiveSfMwithOrthogonalTracksFullProgressiveSfMwithOrthogonalTracksFull ProgressiveSfMProgressiveSfMwithOrthogonalProgressiveSfMwithTwoProgressiveSfMProgressiveSfMwithOrthogonalSfM+MVSonAmbiguousAccurateGround-to-AerialAccurateGround-to-AerialPoints•XiangGao,ShuhanShen,YangZhou,HainanCui,LingjieZhu,ZhanyiHu.AncientChineseArchitecture3DPreservationbyMergingGroundandAerialPointClouds.ISPRSJournalofPhotogrammetryandRemoteSensing,2018XiangGao,LihuaHu,HainanCui,ShuhanShen,ZhanyiHu.AccurateandEfficientGround-to-AerialModelAlignment.PatternRecognition,2018•AccurateGround-to-AerialPointsAccurateGround-to-AerialPointsGroundAerialDirectimagematching(SIFT)isfailedduetonotablechangesinviewpointandDirectpointsregistration(ICP)isfailedduetodifferenceinpointdensityandAccurateGround-to-AerialPointsAccurateGround-to-AerialPointsSystemGround-to-aerialmatchingthroughaerialviewAccurateGround-to-AerialAccurateGround-to-AerialPointsGroundandaerialmergingafterfullAccurateGround-to-AerialAccurateGround-to-AerialPointsGroundandaerialmergingafterMVSandAccurateGround-to-AerialAccurateGround-to-AerialPointsGroundandaerialmergingafterMVSand3DModel3DModelSemanticLabelingbyActive•YangZhou,ShuhanShen,ZhanyiHu.Fine-LevelSemanticLabelingofLarge-Scale3DModelbyActiveLearning.3DV2018(Oral)3DModelSemantic3DModelSemanticLabelingbyActiveBIMLevelSemanticSystem3DModelSemanticLabeling3DModelSemanticLabelingbyActiveBasic••FinetuningatrainedsegmentationCNN(DeeplabV2trainedonCOCO)SelectingasfewimagesaspossibleforhumanannotationwithActiveUsingthemergedsemantic3Dmodelasaweak••••Observationuncertainty:imagesarehardtosegmentationObservationdivergence:imagesaredifferentThe3Dmodelplaysanimportantroleinbatchimages3DModelSemanticLabeling3DModelSemanticLabelingbyActive381318Whatwefound:withthehelpof3Dmodel,handlabeling18images(1632intotal)isadequatetofine-tuneaCNNwith93.5%accuracy.LargeScaleLargeScaleUrbanSceneModelingfromMVS•LingjieZhu,ShuhanShen,XiangGao,ZhanyiHu.LargeScaleUrbanSceneModelingfromMVSMeshes.ECCV2018LingjieZhu,ShuhanShen,ZhanyiHu.VariationalBuildingModelingfromUrbanMVSMeshes.3DV2017•LargeScaleUrbanLargeScaleUrbanSceneModelingfromMVSCityGMLLevelLODModelingofCityLargeScaleUrbanLargeScaleUrbanSceneModelingfromMVSStep1:DetectfacadeStep2:DetectlineLargeScaleUrbanLargeScaleUrbanSceneModelingfromMVSStep3:ShapeStep4:LODLargeScaleLargeScaleUrbanSceneModelingfromMVSInput:94MtrianglefacescoveringanareaofOutput:4343LOD2buildingswith0.79MtriangleLargeScaleLargeScaleUrbanSceneModelingfromMVSInput:94MtrianglefacescoveringanareaofOutput:4343LOD2buildingswith0.79MtriangleThanksThanks第一届全国LA第一届全国LAM技术论坛于218年7月28日-29日成功举办，论坛由中国图象图形学学会主办，深蓝学院发起并承办，浙江大学CAD&CG国家重点实验室协办。本次论坛由浙江大学章国锋教授和清华大学马惠敏副教授担任召集人，邀请香港科技大学沈劭劼老师、阿里巴巴李名杨博士、上海交通大学邹丹平副教授、中国科学院自动化所申抒含副研究员、武汉大学李明副教授、镭神智能技术负责人雷祖芳、图森未来王乃岩博士、纵目科技宋宇博士、地平线SAM负责人杨德刚等10位嘉宾参与交流分享，共来自全国120余所高校和企业的60余人报名参加。论坛为LAM研究者搭建了沟通交流的桥梁，受到了与会者的广泛赞誉。让我们共同期待219年第二届全国SAM技术论坛！嘉宾报告PPT间获取相关资料。第一届全国SLAM技术论坛于2018年7月28日-29浙江大学CAD&CG国家重点实验室协办。本次论坛由浙江大学章国锋教授和清华大学马惠敏副教授担任召集人，邀请香港科技大学沈劭劼老师、阿里巴巴李名杨博士、上海交通大学邹丹平副教授、中国科学院自动化所申抒含副研究员、武汉大学李明副教授、镭神智能技术负责人雷祖第一届全国SLAM技术论坛于2018年7月28日-29浙江大学CAD&CG国家重点实验室协办。本次论坛由浙江大学章国锋教授和清华大学马惠敏副教授担任召集人，邀请香