Learned Intrinsic Auto-Calibration From Fundamental Matrices

Abstract

Auto-calibration that relies on unconstrained image content and epipolar relationships is necessary in online operations, especially when internal calibration parameters such as focal length can vary. In contrast, traditional calibration relies on a checkerboard and other scene information and are typically conducted offline. Unfortunately, auto-calibration may not always converge when solved traditionally in an iterative optimization formalism. We propose to solve for the intrinsic calibration parameters using a neural network that is trained on a synthetic Unity dataset that we created. We demonstrate our results on both synthetic and real data to validate the generalizability of our neural network model, which outperforms traditional methods by 2% to 30%, and outperforms recent deep learning approaches by a factor of 2 to 4 times.