Systems and methods for conditioning training data to avoid learned aberrations

Abstract

System, methods, and other embodiments described herein relate to self-supervised training for monocular depth estimation. In one embodiment, a method includes filtering disfavored images from first training data to produce second training data that is a subsampled version of the first training data. The disfavored images correspond with anomaly maps within a set of depth maps. The first depth model is trained according to the first training data and generates the depth maps from the first training data after initially being trained with the first training data. The method includes training a second depth model according to a self-supervised training process using the second training data. The method includes providing the second depth model to infer distances from monocular images.