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Robust and accurate camera calibration is essential for 3D reconstruction inlight microscopy under circular motion. Conventional methods require eitheraccurate key point matching or precise segmentation of the axial-view images.Both remain challenging because specimens often exhibittransparency/translucency in a light microscope. To address those issues, wepropose a probabilistic inference based method for the camera calibration thatdoes not require sophisticated image pre-processing. Based on 3D projectivegeometry, our method assigns a probability on each of a range of voxels thatcover the whole object. The probability indicates the likelihood of a voxelbelonging to the object to be reconstructed. Our method maximizes a jointprobability that distinguishes the object from the background. Experimentalresults show that the proposed method can accurately recover cameraconfigurations in both light microscopy and natural scene imaging. Furthermore,the method can be used to produce high-fidelity 3D reconstructions and accurate3D measurements.

Probabilistic Inference for Camera Calibration in Light Microscopy under Circular Motion