Main entry points

This repository is divided into different entry points

• Inference: run_megapose_on_example.py is used to run the inference pipeline on a single example image.
• Evaluation: run_full_megapose_eval.py is ued to first run inference on one or several datasets, and then use the results obtained to evaluate the method on these datasets.

Model Zoo

• megapose-1.0-RGB and megapose-1.0-RGBD correspond to method presented and evaluated in the paper.
• -multi-hypothesis is a variant of our approach which:
  • Uses the coarse model, extracts top-K hypotheses (by default K=5);
  • For each hypothesis runs K refiner iterations;
  • Evaluates refined hypotheses using score from coarse model and selects the highest scoring one.
• -icp indicates running ICP refinement on the depth data.

For optimal performance, we recommend using megapose-1.0-RGB-multi-hypothesis for an RGB image and megapose-1.0-RGB-multi-hypothesis-icp for an RGB-D image. An extended paper with full evaluation of these new approaches is coming soon.