potential leaky information was uesd in eval linemod datasets

In eval_linemod.py, the code still use the rmin, rmax, cmin, cmax = get_bbox(meta['obj_bb']) to get the rmin, rmax, cmin, cmax. That’s the important process for the image crop. In my opinion, gt.yaml is the groundtruth for the objects, and the obj_bb is the 2d bounding box. I don’t know whether the code is right. Maybe I was wrong. thank you =。=

[hygxy]

@j96w So you mean the bbox in gt.yaml is actually generated by segnet_results? if that’s the case why do you use the same bbox for training? I am especially confused about the line (122) in dataset.py. The rgb image is cropped into image_masked using bbox from gt.yaml, both for training and evaluation. IMO they should be treated differently, am I right?

[flowtcw]

Hi, after adding 2 more times of iterative refinement, the current testing result on Linemod after fixing the bbox bug is:

Object 1 success rate: 0.9285033365109628 Object 2 success rate: 0.944713870029098 Object 4 success rate: 0.9725490196078431 Object 5 success rate: 0.9409448818897638 Object 6 success rate: 0.9650698602794411 Object 8 success rate: 0.8741328047571854 Object 9 success rate: 0.9305164319248826 Object 10 success rate: 0.9971777986829727 Object 11 success rate: 0.9980694980694981 Object 12 success rate: 0.9248334919124643 Object 13 success rate: 0.9816138917262512 Object 14 success rate: 0.9692898272552783 Object 15 success rate: 0.9606147934678194 ALL success rate: 0.9529245001492092

This result is even higher than the reported 94.3%(all success rate) in the paper. So nothing needs to modify on the paper. Thank you all.

I think that’s a surprise gift. 😃