We introduce a scalable approach for object pose estimation trained on simulated RGB views of multiple 3D models together. We learn an encoding of object views that does not only describe the orientation of all objects seen during training, but can also relate views of untrained objects. Our single-encoder-multi-decoder network is trained using a technique we denote "multi-path learning": While the encoder is shared by all objects, each decoder only reconstructs views of a single object. Consequently, views of different instances do not need to be separated in the latent space and can share common features. The resulting encoder generalizes well from synthetic to real data and across various instances, categories, model types and datasets. We systematically investigate the learned encodings, their generalization capabilities and iterative refinement strategies on the ModelNet40 and T-LESS dataset. On T-LESS, we achieve state-of-the-art results with our 6D Object Detection pipeline, both in the RGB and depth domain, outperforming learning-free pipelines at much lower runtimes.

提出了一种可扩展的对象姿态估计方法，使用多个三维模型的模拟 RGB 视图进行训练，并使用所谓的“多路径学习”技术，涉及共享编码器和不同解码器，从而实现对不同实例的通用编码器的训练。在多个数据集上实现了最先进的 6D 目标检测结果。

跨域对象姿态估计的多路径学习