Method: CosyPose-ECCV20-PBR-1VIEW

User yann_labbe
Publication Labbé et al, CosyPose: Consistent multi-view multi-object 6D pose estimation, ECCV 2020
Implementation https://github.com/ylabbe/cosypose
Training image modalities RGB
Test image modalities RGB
Description

The method is the single-view (1 view) object pose estimation introduced in Labbé et al, CosyPose: Consistent multi-view multi-object 6D pose estimation, ECCV 2020.

For each dataset, we train three networks: a MaskRCNN detector (only 2D detections are used at test time), a model for coarse pose estimation, and a model for iterative refinement. The refinement network is ran for 4 iterations at test time.

Only provided PBR synthetic images are used on each dataset. We add data augmentation to the synthetic images as described in the paper. Pose networks are trained from scratch on all objects. MaskRCNN has a resnet50 FPN backbone pre-trained on COCO.

The timing includes detection and pose estimation on all detections. We do not use the targets file to filter out the detections that are not evaluated.

Computer specifications CPU: 20-core Intel Xeon 6164 @ 3.2 GHz, GPU: Nvidia V100

Public submissions

Date Submission name Dataset
2020-08-18 19:06 - T-LESS
2020-08-18 19:06 - TUD-L
2020-08-18 19:06 - LM-O
2020-08-18 19:07 - IC-BIN
2020-08-18 19:07 - ITODD
2020-08-18 19:07 - HB
2020-08-18 19:08 - YCB-V