MULTI-VIEW CONSISTENCY REGULARIZATION FOR SEMANTIC INTERPRETATION OF EQUAL-RECTANGULAR PANORAMAS

摘要

An artificial neural network is trained to produce spatial labelling for a three-dimensional environment based on image data. A two-dimensional image representation is produced of omni-direction image data captured by one or more cameras of the three-dimensional environment. The artificial neural network is applied using the two-dimensional image representation as input and producing a first predicted label as output. A rotated two-dimensional image is generated by shifting image pixels of the two-dimensional image representation in a horizontal direction. The artificial neural network is then applied again using the rotated two-dimensional image as input and producing a second predicted label as its output. The artificial neural network is trained based at least in part on a difference between the first predicted label and the second predicted label.