Submission: Pix2Pose-BOP19-PBR/LM-O

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Submission name
Submission time (UTC) June 12, 2020, 11:56 a.m.
User kirumang
Task Model-based 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

Method: Pix2Pose-BOP19-PBR

User kirumang
Publication Pix2Pose: Pixel-Wise Coordinate Regression of Objects for 6D Pose Estimation, ICCV 2019
Training image modalities RGB
Test image modalities RGB

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