  We propose a simple three-stage approach to segment unseen objects in RGB
images using their CAD models. Leveraging recent powerful foundation models,
DINOv2 and Segment Anything, we create descriptors and generate proposals,
including binary masks for a given input RGB image. By matching proposals with
reference descriptors created from CAD models, we achieve precise object ID
assignment along with modal masks. We experimentally demonstrate that our
method achieves state-of-the-art results in CAD-based novel object
segmentation, surpassing existing approaches on the seven core datasets of the
BOP challenge by 19.8% AP using the same BOP evaluation protocol. Our source
code is available at https://github.com/nv-nguyen/cnos.
