| Submission name | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Submission time (UTC) | June 12, 2020, 11:56 a.m. | ||||||||||
| User | kirumang | ||||||||||
| Task | 6D localization of seen objects | ||||||||||
| Dataset | LM-O | ||||||||||
| Training model type | Default | ||||||||||
| Training image type | Synthetic (only PBR images provided for BOP Challenge 2020 were used) | ||||||||||
| Description | Conclusion: Using PBR images for training (instead of given synthetic images) dramatically improves the performance for our method (BOP score: 0.077 -> 0.281) . I highly recommend others to use PBR images for their training! | ||||||||||
| Evaluation scores |
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| User | kirumang |
|---|---|
| Publication | Pix2Pose: Pixel-Wise Coordinate Regression of Objects for 6D Pose Estimation, ICCV 2019 |
| Implementation | https://github.com/kirumang/Pix2Pose |
| Training image modalities | RGB |
| Test image modalities | RGB |
| Description | The results are obtained using the same code used for BOP'19 with the original paper backbone (trained from scratch, no pre-trained weights are used for Pix2Pose, for Mask-RCNN : MScoco pre-trained weights are used) This is to directly compare the effect of PBR images while using the exactly the same parameters for training and evaluations that we used for BOP'19 challenge. (In the BOP'19, we used synthetic images that are provided in the challenge without additional renderings) |
| Computer specifications | intel i7-6700K, NVIDA GTX 1080, 16GB RAM |