Hi,
I'm looking at demo.py codes, and when I ran it to test model's performance on ycbv dataset, I found the pretrained model couldn't segment objects correctly. For example, the GT object class ids should be [1, 4, 6, 19, 20], but the pred_cls_ids was [ 1 6 7 9 13 14 17 18 19 20 21]. I'd like to know how to solve this problem? Thank you!
Our segmentation branch is a kind of bottom-up method, which may lead to some false-positive results. There are some ways to filter out these false positives:
If the objects types in the scene are known as in some manipulation tasks, you can only select the results of target classes.
If not, ways to reject the false positives include:
Increase the threshold of the minimum number of object points here. That may also lead to missing of some heavily occluded objects or small objects that have little visible points.
Use the estimated object pose and mesh models to calculate loss with the scene point clouds and filter out objects with a designed threshold.
Hi, I'm looking at demo.py codes, and when I ran it to test model's performance on ycbv dataset, I found the pretrained model couldn't segment objects correctly. For example, the GT object class ids should be [1, 4, 6, 19, 20], but the pred_cls_ids was [ 1 6 7 9 13 14 17 18 19 20 21]. I'd like to know how to solve this problem? Thank you!