Fusing RGB and depth with Self-attention for Unseen Object Segmentation

We present a Synthetic RGB-D Fusion Mask R-CNN (SF Mask R-CNN) for unseen object instance segmentation. Our key idea is to fuse RGB and depth with a learnable spatial attention estimator, named Self-Attention-based Confidence map Estimator (SACE), in four scales upon a category-agnostic instance segmentation model. We pre-trained this SF Mask R-CNN on a large synthetic dataset and evaluated it on a public dataset, WISDOM, after fine-tuning on only a small number of real-world datasets. Our experiments showed the state-of-the-art performance of SACE in unseen object segmentation. Also, we compared the feature maps varying the input modality and fusion method and showed that SACE could be helpful to learn distinctive object-related features. The codes, dataset, and models are available at https://github.com/gist-ailab/SF-Mask-RCNN.

Automated summary

The study introduces a novel Synthetic RGB-D Fusion Mask R-CNN model for unseen object instance segmentation by utilizing a learnable spatial attention estimator. Experimental results demonstrate its state-of-the-art performance in unseen object segmentation.