Monocular depth prediction and occupancy correction systems and methods for item stow processes

Abstract

Monocular depth prediction machine learning models may be trained to receive imaging data of inventory holders having translucent bands that at least partially occlude portions of compartments and items of the inventory holders. The monocular depth prediction models may process the imaging data to generate accurate and reliable depth maps for the inventory holders, while substantially ignoring or disregarding the translucent, partially occlusive bands. Further, the depth maps may be utilized by various occupancy correction models to improve determinations related to available storage space within compartments of the inventory holders, thereby improving the accuracy and efficiency of various material handling processes.