Reconstructing 3D models for single objects with complex backgrounds has wide applications like 3D printing,AR/VR,and so on.It is necessary to consider the tradeoff between capturing data at low cost and getting high-...Reconstructing 3D models for single objects with complex backgrounds has wide applications like 3D printing,AR/VR,and so on.It is necessary to consider the tradeoff between capturing data at low cost and getting high-quality reconstruction results.In this work,we propose a voxel-based modeling pipeline with sparse RGB-D images to effectively and efficiently reconstruct a single real object without the geometrical post-processing operation on background removal.First,referring to the idea of VisualHull,useless and inconsistent voxels of a targeted object are clipped.It helps focus on the target object and rectify the voxel projection information.Second,a modified TSDF calculation and voxel filling operations are proposed to alleviate the problem of depth missing in the depth images.They can improve TSDF value completeness for voxels on the surface of the object.After the mesh is generated by the MarchingCube,texture mapping is optimized with view selection,color optimization,and camera parameters fine-tuning.Experiments on Kinect capturing dataset,TUM public dataset,and virtual environment dataset validate the effectiveness and flexibility of our proposed pipeline.展开更多
针对传统人数统计方法因遮挡、光照变化导致准确率低的问题,提出一种适用于深度图的模拟降水分水岭算法(Depth map based Rainfalling Watershed Segmentation,D-RWS)。修复深度图并用混合高斯背景建模提取前景。利用D-RWS算法分割深度...针对传统人数统计方法因遮挡、光照变化导致准确率低的问题,提出一种适用于深度图的模拟降水分水岭算法(Depth map based Rainfalling Watershed Segmentation,D-RWS)。修复深度图并用混合高斯背景建模提取前景。利用D-RWS算法分割深度图中感兴趣的行人头部区域(Region Of Interest,ROI)。采用质心欧式距离最短法关联各帧中同一目标并跟踪计数。实验结果表明:提出的方法准确率能够达到98%以上,平均每帧处理时间为25 ms(40 f/s),准确率和实时性可满足实际应用的要求。展开更多
基金supported by the Key Technological Innovation Projects of Hubei Province,China(No.2018AAA062)the National Natural Science Foundation of China(No.61972298)+1 种基金the Ministry of Education of Humanities and Social Sciences Project,China(No.17YJC760124)the Scientific Research Project of Department of Education of Hubei Province,China(No.B2021278).
文摘Reconstructing 3D models for single objects with complex backgrounds has wide applications like 3D printing,AR/VR,and so on.It is necessary to consider the tradeoff between capturing data at low cost and getting high-quality reconstruction results.In this work,we propose a voxel-based modeling pipeline with sparse RGB-D images to effectively and efficiently reconstruct a single real object without the geometrical post-processing operation on background removal.First,referring to the idea of VisualHull,useless and inconsistent voxels of a targeted object are clipped.It helps focus on the target object and rectify the voxel projection information.Second,a modified TSDF calculation and voxel filling operations are proposed to alleviate the problem of depth missing in the depth images.They can improve TSDF value completeness for voxels on the surface of the object.After the mesh is generated by the MarchingCube,texture mapping is optimized with view selection,color optimization,and camera parameters fine-tuning.Experiments on Kinect capturing dataset,TUM public dataset,and virtual environment dataset validate the effectiveness and flexibility of our proposed pipeline.
文摘针对传统人数统计方法因遮挡、光照变化导致准确率低的问题,提出一种适用于深度图的模拟降水分水岭算法(Depth map based Rainfalling Watershed Segmentation,D-RWS)。修复深度图并用混合高斯背景建模提取前景。利用D-RWS算法分割深度图中感兴趣的行人头部区域(Region Of Interest,ROI)。采用质心欧式距离最短法关联各帧中同一目标并跟踪计数。实验结果表明:提出的方法准确率能够达到98%以上,平均每帧处理时间为25 ms(40 f/s),准确率和实时性可满足实际应用的要求。
