Learning Object Localization and 6D Pose Estimation from Simulation and Weakly Labeled Real Images

TL;DR

This paper presents a method for training an object detector and 6D pose estimator using synthetic data and minimal weakly labeled real images, employing domain adaptation to improve real-world performance for robotic manipulation tasks.

Contribution

The authors introduce a novel training pipeline combining simulation, weakly labeled real images, and adversarial domain adaptation for effective 6D pose estimation.

Findings

Significant performance improvement with minimal real data.

Effective domain adaptation reduces synthetic-real gap.

Applicable to cluttered, occluded scenes in robotics.

Abstract

This work proposes a process for efficiently training a point-wise object detector that enables localizing objects and computing their 6D poses in cluttered and occluded scenes. Accurate pose estimation is typically a requirement for robust robotic grasping and manipulation of objects placed in cluttered, tight environments, such as a shelf with multiple objects. To minimize the human labor required for annotation, the proposed object detector is first trained in simulation by using automatically annotated synthetic images. We then show that the performance of the detector can be substantially improved by using a small set of weakly annotated real images, where a human provides only a list of objects present in each image without indicating the location of the objects. To close the gap between real and synthetic images, we adopt a domain adaptation approach through adversarial training. The detector resulting from this training process can be used to localize objects by using its per-object activation maps. In this work, we use the activation maps to guide the search of 6D poses of objects. Our proposed approach is evaluated on several publicly available datasets for pose estimation. We also evaluated our model on classification and localization in unsupervised and semi-supervised settings. The results clearly indicate that this approach could provide an efficient way toward fully automating the training process of computer vision models used in robotics.