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Efficient 3D voxel reconstruction of human shape within robotic work cells

机译:在机器人工作单元中高效地对人体形状进行3D体素重建

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摘要

In this paper an efficient voxel reconstruction method within a robotic work cell by means of shape from silhouette is presented. A procedure is proposed which includes occlusions from the robot's kinematics into the reconstruction step, i.e. the time-dependent occupied space. Axis values together with surface models from the robot are used for modeling occlusions coming from the moving device. An image-based method for simplified in-place frame calibration is shown and compared to a regular marker-based method. A learning method for background models is introduced, which includes overlapping image areas from the robot and offers improved learning rates. For efficiency reasons the implementation was done on the GPU. The achieved performance using five cameras in an industrial setting is shown and discussed with respect to quality and framerates.
机译:在本文中,提出了一种有效的在机器人工作单元内利用轮廓形状进行体素重建的方法。提出了一种程序,该程序包括从机器人运动学到重建步骤的阻塞,即与时间有关的占用空间。轴值与机器人的曲面模型一起用于对来自移动设备的遮挡进行建模。示出了用于简化就地帧校准的基于图像的方法,并将其与基于常规标记的方法进行比较。介绍了一种用于背景模型的学习方法,其中包括来自机器人的重叠图像区域,并提供了提高的学习率。出于效率原因,该实现是在GPU上完成的。展示并讨论了在工业环境中使用五台摄像机获得的性能,并针对质量和帧速率进行了讨论。

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