Automatic Depth Image Generation for Unseen Object Amodal Instance Segmentation

This research proposes an automatic instance-wise depth image estimation method for Unseen Object Amodal Instance Segmentation (UOAIS), which predicts depth images automatically by sharing features of other prediction branches. The depth images can provide valuable information for amodal mask generation. To achieve good performance, the model estimates the depth images in two steps by adding two extra depth branches to the original UOAIS model. A new feature fusion scheme incorporating these two branches is also designed to facilitate information sharing between tasks. The new model is evaluated on OCID and UOAIS-Sim evaluation datasets. Compared with the performance of the original model with pure RGB input, the AP50 of the new model increases by 0.685% on the UOAIS-Sim evaluation dataset, and the overlap and boundary F measures increase by 2.3% and 1.6% respectively on the OCID dataset. Consequently, the model achieves similar performance to those which need RGB-D images as input. It has the potential to serve as a compromise plan for UOAIS if the collection of real scene-depth images is difficult or unavailable.