  Robust and accurate camera calibration is essential for 3D reconstruction in
light microscopy under circular motion. Conventional methods require either
accurate key point matching or precise segmentation of the axial-view images.
Both remain challenging because specimens often exhibit
transparency/translucency in a light microscope. To address those issues, we
propose a probabilistic inference based method for the camera calibration that
does not require sophisticated image pre-processing. Based on 3D projective
geometry, our method assigns a probability on each of a range of voxels that
cover the whole object. The probability indicates the likelihood of a voxel
belonging to the object to be reconstructed. Our method maximizes a joint
probability that distinguishes the object from the background. Experimental
results show that the proposed method can accurately recover camera
configurations in both light microscopy and natural scene imaging. Furthermore,
the method can be used to produce high-fidelity 3D reconstructions and accurate
3D measurements.
