| Submission name | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Submission time (UTC) | Dec. 22, 2021, 6:09 p.m. | ||||||||||
| User | surfemb | ||||||||||
| Task | 6D localization of seen objects | ||||||||||
| Dataset | ITODD | ||||||||||
| Training model type | Default | ||||||||||
| Training image type | Synthetic (only PBR images provided for BOP Challenge 2020 were used) | ||||||||||
| Description | |||||||||||
| Evaluation scores |
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| User | surfemb |
|---|---|
| Publication | Rasmus Laurvig Haugaard, Anders Glent Buch: SurfEmb, CVPR 2022 |
| Implementation | https://github.com/rasmushaugaard/surfemb |
| Training image modalities | RGB |
| Test image modalities | RGB-D |
| Description | SurfEmb: Dense and Continuous Correspondence Distributions for Object Pose Estimation with Learnt Surface Embeddings Compared to the paper:
We use the available detections from CosyPose, but were not able to find detection-only timings. We add our timing on top of the full CosyPose single-view RGB pipeline, resulting in conservative timings. |
| Computer specifications | 1 x RTX 2080 |