Project Summary

Part segmentation can reduce the ambiguity of meshes used in further downstream tasks for AR/VR at Meta.  Multi-view(MV) part segmentation faces challenges due to complexity and high labeling costs/time (can take up to 1 min/annotation). Common failure cases at Meta and in our current pipeline involve self-occlusions, crossings between hands and feet, and complex joint actions where boundaries are unsure (forearm/upper arm under complex elbow rotations). We aim to construct novel active learning (AL) approaches to identify hard examples, maintaining ~95% of possible accuracy while reducing costs (with only ~30-35% annotated data). By selectively querying informative samples, we plan to leverage MV information to accelerate learning, offering an efficient solution for MV part segmentation. 

Examples of self-occlusions and complex actions: