The present application relates to the field of optical precision measurement, in particular to a rotation and translation
absolute calibration method based on neural
radiation field, medium and equipment. The method steps are: obtaining at least three groups of interference measurement data of the measured mirror under the original, rotation and translation
pose of the to-be-calibrated interference measurement
system, inputting the pixel coordinates and position coding information in the data into the neural
radiation field network, optimizing the network parameters through the self-
supervised training strategy, and completing the calibration of the
reference mirror surface. Compared with the traditional Zernike method, the present scheme adopts a neural
radiation field to construct a double-
branch network, can adaptively learn the complex
surface shape space distribution, and improve the separation accuracy of medium and
high frequency errors; the double-
branch synchronously directly outputs the errors of the
reference mirror and the measured mirror, without dependence on the sequence, avoiding error transmission and accumulation; the self-
supervised training mechanism implicitly compensates the
pose deviation and
noise interference through redundant constraints, and shows higher
engineering robustness.