add(average distance) metric is computed by
visiineeds to be installed here.
- Downlad neural network weights (in
.pth) and save them to the
contentfolder to replace proxy files. Note that we only provide the weights and models (already in
content/models/grocery) for the
Cornobject for now.
We provide some demo images in
uncertainty_quantification/output/test for code test and demonstrations. These demo images are generated by the NVISII render. There are two example scripts:
uncertainty_quantification/run.pyrequires the ground truth poses for statistics. This script would first do pose estimation based on DOPE (but you do not need to install DOPE or ROS), and then do post-inference uncertainty quantification. The expected result is that this script would generate all files in
uncertainty_quantification/output/test_result, including inference results, confidence plot, the most confident frame selection, uncertainty quantification correlation analysis, etc.
uncertainty_quantification/run_realworld.pyis similar, but do not need the ground truth poses. The expected result is that this script would generate all files in
uncertainty_quantification/output/test_result_realworld. This script corresponds to the real-world grasping experiment in our paper, where there is no ground truth pose estimation.