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We introduce a scalable approach for object pose estimation trained onsimulated RGB views of multiple 3D models together. We learn an encoding ofobject views that does not only describe an implicit orientation of all objectsseen during training, but can also relate views of untrained objects. Oursingle-encoder-multi-decoder network is trained using a technique we denote"multi-path learning": While the encoder is shared by all objects, each decoderonly reconstructs views of a single object. Consequently, views of differentinstances do not have to be separated in the latent space and can share commonfeatures. The resulting encoder generalizes well from synthetic to real dataand across various instances, categories, model types and datasets. Wesystematically investigate the learned encodings, their generalization, anditerative refinement strategies on the ModelNet40 and T-LESS dataset. Despitetraining jointly on multiple objects, our 6D Object Detection pipeline achievesstate-of-the-art results on T-LESS at much lower runtimes than competingapproaches.

Multi-path Learning for Object Pose Estimation Across Domains