Method: SMC-1.0s-CNOS

User Tuebel
Training image modalities None
Test image modalities D

Submitted to: BOP Challenge 2023

Training data: None

Onboarding data: None

Used 3D models: CAD or default


The localization-only method requires a separate detector and is only tested on Task 4 with default segmentation masks from CNOS. This version runs the pose inference at most for inst_count instances from test_targets_bop19.json.

It uses a sequential Monte Carlo (SMC) sampler and the CAD model to estimate the pose using only the segmentation mask and the depth image. The GPU is utilized to render depth images for pose hypotheses and evaluate their likelihood in parallel.

The sampler had a time budget of 1.0s per object pose inference.

Part of my dissertation which will be published with code in 2024. Contact:

Computer specifications AMD Ryzen Threadripper PRO 5975WX, NVIDIA RTX 4090

Public submissions

Date Submission name Dataset
2023-11-12 10:49 - LM-O
2023-11-12 10:50 - ITODD
2023-11-12 10:50 - T-LESS
2023-11-12 10:50 - TUD-L
2023-11-12 10:50 - IC-BIN
2023-11-12 10:50 - HB
2023-11-12 10:51 - YCB-V