Depth Auto-calibration for Range Cameras Based on 3D Geometry Reconstruction
An approach for auto-calibration and validation of depth measurements gained from range cameras is introduced. Firstly, the geometry of the scene is reconstructed and its surface normals are computed. These normal vectors are segmented in 3D with the Mean-Shift algorithm and large planes like walls or the ground plane are recovered. The 3D reconstruction of the scene geometry is then utilized in a novel approach to derive principal camera parameters for range or depth cameras. It operates based on a single range image alone and does not require special equipment such as markers or a checkerboard and no specific measurement procedures as are necessary for previous methods. The fact that wrong camera parameters deform the geometry of the objects in the scene is utilized to infer the constant depth error (the phase offset for continuous wave ToF cameras) as well as the focal length. The proposed method is applied to ToF cameras which are based on the Photonic Mixer Device to measure the depth of objects in the scene. Its capabilities as well as its current and systematic limitations are addressed and demonstrated.
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