Reconstruction of man-made scenes from multi-view images is an important problem in computer vision and computer graphics.Observing that manmade scenes are usually composed of planar surfaces,we encode plane shape pri...Reconstruction of man-made scenes from multi-view images is an important problem in computer vision and computer graphics.Observing that manmade scenes are usually composed of planar surfaces,we encode plane shape prior in reconstructing man-made scenes.Recent approaches for single-view reconstruction employ multi-branch neural networks to simultaneously segment planes and recover 3D plane parameters.However,the scale of available annotated data heavily limits the generalizability and accuracy of these supervised methods.In this paper,we propose multiview regularization to enhance the capability of piecewise planar reconstruction during the training phase,without demanding extra annotated data.Our multi-view regularization enables the consistency among multiple views by making the feature embedding more robust against view change and lighting variations.Thus,the neural network trained by multi-view regularization performs better on a wide range of views and lightings in the test phase.Based on more consistent prediction results,we merge the recovered models from multiple views to reconstruct scenes.Our approach achieves state-of-the-art reconstruction performance compared to previous approaches on the public Scan Net dataset.展开更多
The consistency of the CIMS multi-view model is a complicated problem which plays an important role inthe CIMS life cycle. Based on the function-oriented multi-view modeling approach, an object-oriented integratedmult...The consistency of the CIMS multi-view model is a complicated problem which plays an important role inthe CIMS life cycle. Based on the function-oriented multi-view modeling approach, an object-oriented integratedmulti-view modeling approach was further provided.CIM system is composed of several objects where each objecthas a multi-view description.The four most important view-points: function, information, resource and dynamic viewpoint were chosen to provide conceptual model of the object-oriented integrated multi-view modeling in CIMS using a system integration approach展开更多
同一目标在不同观察视点下成像后外形可能有较大差异,因此三维目标多视点视图建模是目标识别的关键.针对该问题,提出了基于支持向量数据描述(SVDD,SupportVector Data Description)方法对目标特征进行描述.在视点球面上均匀采样获取目...同一目标在不同观察视点下成像后外形可能有较大差异,因此三维目标多视点视图建模是目标识别的关键.针对该问题,提出了基于支持向量数据描述(SVDD,SupportVector Data Description)方法对目标特征进行描述.在视点球面上均匀采样获取目标全姿态图像,以SVDD方法求取在高维空间内包含尽可能多目标特征向量的最小超球体相关参数,得到数量较少的支持向量将作为目标多视点视图的最佳模型.对多类目标不同姿态的图像(每类2592帧),以规则化不变矩描述目标外形特征,进行了建模实验,并通过识别实验验证了所提方法的有效性和可行性.展开更多
基金supported by the National Key R&D Program of China under Grant 2017YFB1002202the National Natural Science Foundation of China(NSFC)under Grant 61632006the Fundamental Research Funds for the Central Universities under Grants WK3490000003 and WK2100100030.
文摘Reconstruction of man-made scenes from multi-view images is an important problem in computer vision and computer graphics.Observing that manmade scenes are usually composed of planar surfaces,we encode plane shape prior in reconstructing man-made scenes.Recent approaches for single-view reconstruction employ multi-branch neural networks to simultaneously segment planes and recover 3D plane parameters.However,the scale of available annotated data heavily limits the generalizability and accuracy of these supervised methods.In this paper,we propose multiview regularization to enhance the capability of piecewise planar reconstruction during the training phase,without demanding extra annotated data.Our multi-view regularization enables the consistency among multiple views by making the feature embedding more robust against view change and lighting variations.Thus,the neural network trained by multi-view regularization performs better on a wide range of views and lightings in the test phase.Based on more consistent prediction results,we merge the recovered models from multiple views to reconstruct scenes.Our approach achieves state-of-the-art reconstruction performance compared to previous approaches on the public Scan Net dataset.
文摘The consistency of the CIMS multi-view model is a complicated problem which plays an important role inthe CIMS life cycle. Based on the function-oriented multi-view modeling approach, an object-oriented integratedmulti-view modeling approach was further provided.CIM system is composed of several objects where each objecthas a multi-view description.The four most important view-points: function, information, resource and dynamic viewpoint were chosen to provide conceptual model of the object-oriented integrated multi-view modeling in CIMS using a system integration approach
文摘同一目标在不同观察视点下成像后外形可能有较大差异,因此三维目标多视点视图建模是目标识别的关键.针对该问题,提出了基于支持向量数据描述(SVDD,SupportVector Data Description)方法对目标特征进行描述.在视点球面上均匀采样获取目标全姿态图像,以SVDD方法求取在高维空间内包含尽可能多目标特征向量的最小超球体相关参数,得到数量较少的支持向量将作为目标多视点视图的最佳模型.对多类目标不同姿态的图像(每类2592帧),以规则化不变矩描述目标外形特征,进行了建模实验,并通过识别实验验证了所提方法的有效性和可行性.
