WS-OPE: Weakly Supervised 6-D Object Pose Regression Using Relative Multi-Camera Pose Constraints

Precise annotation of 6-D poses in real data is intricate and time-consuming, however, an essential requirement to train pose estimation pipelines. We propose a way for scalable, end-to-end 6-D pose regression with weak supervision to avoid this problem. Our method requires neither 3-D models nor 6-D object poses as ground truth. Instead, we use 2-D bounding boxes and object sizes as the only labels and constrain the problem with multiple images of known relative poses during training. A novel Rotated-IoU loss brings together a pose prediction from an image with labeled 2-D bounding boxes of the corresponding object in other views. Our rotation estimation combines an initial coarse pose classification with an offset regression using a continuous rotation parametrization that allows for direct pose estimation. At test time, the model still uses only a single image to predict a 6-D pose. We observe that multi-view constraints and our rotation representation used during training lead to better learning of 6-D pose embeddings in comparison to fully supervised methods. Experiments on several datasets show that the proposed method is capable of predicting poses of good quality, in spite being trained with only weak labels. Direct pose regression without the need for a consecutive refinement stage thereby ensures real-time performance.

Automated summary

A novel scalable, end-to-end 6-D pose regression method with weak supervision is proposed, which uses 2-D bounding boxes and object sizes as the only labels and constraints during training with multiple images of known relative poses, leading to better learning of 6-D pose embeddings compared to fully supervised methods and ensuring real-time performance by direct pose regression.