Probabilistic Inference for Camera Calibration in Light Microscopy under Circular Motion

TL;DR

This paper introduces a probabilistic inference method for calibrating cameras in light microscopy under circular motion, eliminating the need for complex image pre-processing and improving 3D reconstruction accuracy.

Contribution

It presents a novel probabilistic approach based on 3D projective geometry that accurately calibrates cameras without requiring key point matching or segmentation.

Findings

Accurately recovers camera configurations in microscopy and natural scenes

Enables high-fidelity 3D reconstructions

Provides precise 3D measurements

Abstract

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.