Submitted to: BOP Challenge 2023

Training data: MegaPose-GSO and MegaPose-ShapeNetCore

Onboarding data: No

Used 3D models: Default, CAD

Notes:

In this submission, CNOS_fastSAM [A] detections are used as the input to our pose estimation method. Our pose estimation method uses the coarse-to-fine strategy following the MegaPose [B] structure. A single model is used for all datasets.

Our coarse network is based on the MegaPose [B] coarse network. The main differences from the original MegaPose paper are as follows:

• To bypass the computation of rendering tons of images, we render fewer images and run the coarse network. Then we create a GMM with the top-k hypotheses and run the coarse network to the sampled hypotheses from the GMM.

[A] Nguyen et al.: CNOS: A Strong Baseline for CAD-based Novel Object Segmentation, arXiv 2023
[B] Labbé et al.: MegaPose: 6D Pose Estimation of Novel Objects via Render & Compare, CoRL 2022

List of contributors: Sungphill Moon (sungphill.moon@naverlabs.com), Hyeontae Son (son.ht@naverlabs.com)

If you have any questions, feel free to contact us.