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.
@article{arxiv.2307.11067,
title = {CNOS: A Strong Baseline for CAD-based Novel Object Segmentation},
author = {Van Nguyen Nguyen and Thibault Groueix and Georgy Ponimatkin and Vincent Lepetit and Tomas Hodan},
journal= {arXiv preprint arXiv:2307.11067},
year = {2023}
}