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- W3136338304 abstract "In this work, we introduce a new calibration method for a camera system comprising five Azure Kinect. The calibration method uses a ChArUco coded cube installed in the middle of the system. A new 3D optimization cost is proposed to overcome the IR camera noise and to enhance global 3D consistency of the captured model. The cost includes the repro-jection error and the point to plane distance. As a refinement stage, along with point to plane distance, a patch to plane distance is added in the cost to overcome the noise effect of the depth camera. The experimental results demonstrate that the proposed calibration method achieves a better reprojection error and more stable results in terms of standard deviation of the estimated pose compared to the state-of-the-art. In addition, the qualitative results show that the proposed method can produce a better registered point cloud compared to conventional calibration." @default.
- W3136338304 created "2021-03-29" @default.
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- W3136338304 date "2020-12-15" @default.
- W3136338304 modified "2023-09-26" @default.
- W3136338304 title "Robust Calibration of a Multi-View Azure Kinect Scanner Based on Spatial Consistency" @default.
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- W3136338304 doi "https://doi.org/10.1109/ic3d51119.2020.9376321" @default.
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