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This work proposes a process for efficiently training a point-wise objectdetector that enables localizing objects and computing their 6D poses incluttered and occluded scenes. Accurate pose estimation is typically arequirement for robust robotic grasping and manipulation of objects placed incluttered, tight environments, such as a shelf with multiple objects. Tominimize the human labor required for annotation, the proposed object detectoris first trained in simulation by using automatically annotated syntheticimages. We then show that the performance of the detector can be substantiallyimproved by using a small set of weakly annotated real images, where a humanprovides only a list of objects present in each image without indicating thelocation of the objects. To close the gap between real and synthetic images, weadopt a domain adaptation approach through adversarial training. The detectorresulting from this training process can be used to localize objects by usingits per-object activation maps. In this work, we use the activation maps toguide the search of 6D poses of objects. Our proposed approach is evaluated onseveral publicly available datasets for pose estimation. We also evaluated ourmodel on classification and localization in unsupervised and semi-supervisedsettings. The results clearly indicate that this approach could provide anefficient way toward fully automating the training process of computer visionmodels used in robotics.

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