This research project implements a self-calibration algorithm for generic cameras with non-linear distortions by combining Neural Radiance Fields with camera model estimation. The system uses a pinhole model with radial distortion and a learnable noise model to handle arbitrary non-linear camera distortions. It incorporates both photometric consistency (via NeRF) and geometric constraints through a projected ray distance loss function, enabling camera calibration without calibration objects.